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Kim, Minkyoung, Lexing Xie, and Peter Christen. "Event Diffusion Patterns in Social Media." Proceedings of the International AAAI Conference on Web and Social Media 6, no. 1 (August 3, 2021): 178–85. http://dx.doi.org/10.1609/icwsm.v6i1.14248.
Повний текст джерелаАнотація:
This study focuses on real-world events and their reflections on the continuous stream of online discussions. Studying event diffusion on social media is important, as this will tell us how a significant event (such as a natural disaster) spreads and evolves interacting with other events, and who has helped spreading the event. Tracking an ever-changing list of often unanticipated events is difficult, and most prior work has focused on specific event derivatives such as quotes or user-generated tags. In this paper, we propose a method for identifying real-world events on social media, and present observations about event diffusion patterns across diverse media types such as news, blogs, and social networking sites. We first construct an event registry based on the Wikipedia portal of global news events, and we represent each real-world event with entities that embody the 5W1H (e.g., organization, person name, place) used in news coverage. We then label each web document with the list of identified events based on entity similarity between them. We analyze the ICWSM’11 Spinn3r dataset containing over 60 million English documents. We observe surprising connections among the 161 events it covers, and that over half (55%) of users only link to a small fraction of prolific users (4%), a notable departure from the balanced traditional bow-tie model of web content.
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Ma, Long, Chei Sian Lee, and Dion Hoe-Lian Goh. "Understanding news sharing in social media." Online Information Review 38, no. 5 (July 7, 2014): 598–615. http://dx.doi.org/10.1108/oir-10-2013-0239.
Повний текст джерелаАнотація:
Purpose – The purpose of this paper is to draw from the diffusion of innovations theory to explore multi-levels of influences (i.e. individuals, networks, news attributes) on news sharing in social media. Design/methodology/approach – A survey was designed and administered to 309 respondents. Structural equation modelling analysis was conducted to examine the three levels of influential factors. These included self-perceptions of opinion leadership and seeking at the individual level, perceived tie strength and homophily at the network level, and finally, perceived news credibility and news preference at the news attribute level. Findings – The results revealed that the influences of self-perceptions of opinion leadership, perceived tie strength in online networks and perceived preference of online news had significant effects on users’ news sharing intention in social media. However, self-perceptions of opinion seeking, homophily, and perceived news credibility were not significant. Originality/value – This is one of the first studies on news sharing in social media that focus on diverse levels of influential factors. In particular, the research suggests the viability of the diffusion of innovations theory to explain this pervasive global phenomenon. Further, the influential factors identified may help to stimulate active participation in social media platforms and ultimately enhance the sustainability of these platforms.
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Allcott, Hunt, Matthew Gentzkow, and Chuan Yu. "Trends in the diffusion of misinformation on social media." Research & Politics 6, no. 2 (April 2019): 205316801984855. http://dx.doi.org/10.1177/2053168019848554.
Повний текст джерелаАнотація:
In recent years, there has been widespread concern that misinformation on social media is damaging societies and democratic institutions. In response, social media platforms have announced actions to limit the spread of false content. We measure trends in the diffusion of content from 569 fake news websites and 9540 fake news stories on Facebook and Twitter between January 2015 and July 2018. User interactions with false content rose steadily on both Facebook and Twitter through the end of 2016. Since then, however, interactions with false content have fallen sharply on Facebook while continuing to rise on Twitter, with the ratio of Facebook engagements to Twitter shares decreasing by 60%. In comparison, interactions with other news, business, or culture sites have followed similar trends on both platforms. Our results suggest that the relative magnitude of the misinformation problem on Facebook has declined since its peak.
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Kim, Minkyoung, David Newth, and Peter Christen. "Modeling Dynamics of Diffusion Across Heterogeneous Social Networks: News Diffusion in Social Media." Entropy 15, no. 12 (October 8, 2013): 4215–42. http://dx.doi.org/10.3390/e15104215.
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Ahmed, Wasim, and Sergej Lugovic. "Social media analytics: analysis and visualisation of news diffusion using NodeXL." Online Information Review 43, no. 1 (February 11, 2019): 149–60. http://dx.doi.org/10.1108/oir-03-2018-0093.
Повний текст джерелаАнотація:
Purpose The purpose of this paper is to provide an overview of NodeXL in the context of news diffusion. Journalists often include a social media dimension in their stories but lack the tools to get digital photos of the virtual crowds about which they write. NodeXL is an easy to use tool for collecting, analysing, visualising and reporting on the patterns found in collections of connections in streams of social media. With a network map patterns emerge that highlight key people, groups, divisions and bridges, themes and related resources. Design/methodology/approach This study conducts a literature review of previous empirical work which has utilised NodeXL and highlights the potential of NodeXL to provide network insights of virtual crowds during emerging news events. It then develops a number of guidelines which can be utilised by news media teams to measure and map information diffusion during emerging news events. Findings One emergent software application known as NodeXL has allowed journalists to take “group photos” of the connections among a group of users on social media. It was found that a diverse range of disciplines utilise NodeXL in academic research. Furthermore, based on the features of NodeXL, a number of guidelines were developed which provide insight into how to measure and map emerging news events on Twitter. Social implications With a set of social media network images a journalist can cover a set of social media content streams and quickly grasp “situational awareness” of the shape of the crowd. Since social media popular support is often cited but not documented, NodeXL social media network maps can help journalists quickly document the social landscape utilising an innovative approach. Originality/value This is the first empirical study to review literature on NodeXL, and to provide insight into the value of network visualisations and analytics for the news media domain. Moreover, it is the first empirical study to develop guidelines that will act as a valuable resource for newsrooms looking to acquire insight into emerging news events from the stream of social media posts. In the era of fake news and automated accounts, i.e., bots the ability to highlight opinion leaders and ascertain their allegiances will be of importance in today’s news climate.
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Provaznik, Daniel, and Jillian Wisniewski. "Modeling Diffusion of Information in an Increasingly Complex Digital Domain." Industrial and Systems Engineering Review 6, no. 2 (March 7, 2019): 126–34. http://dx.doi.org/10.37266/iser.2018v6i2.pp126-134.
Повний текст джерелаАнотація:
Offering entertainment, discussion, and information, social media provides users with a stimulating online experience. Within the last five years, it has also become an increasingly popular medium for the consumption of news. News outlets publish articles and reports through social media, and by doing so influence their users in a way that corresponds with the outlet’s political leaning. Because social media outlets provide users with tailored content, the prevalence of biased news reporting reinforces the user’s political values and polarizes their beliefs. This thesis attempts to examine the relationships that give rise to this political polarization in social media and discusses possible opportunities to mitigate it.
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Sharma, Karishma, Xinran He, Sungyong Seo, and Yan Liu. "Network Inference from a Mixture of Diffusion Models for Fake News Mitigation." Proceedings of the International AAAI Conference on Web and Social Media 15 (May 22, 2021): 668–79. http://dx.doi.org/10.1609/icwsm.v15i1.18093.
Повний текст джерелаАнотація:
The dissemination of fake news intended to deceive people, influence public opinion and manipulate social outcomes, has become a pressing problem on social media. Moreover, information sharing on social media facilitates diffusion of viral information cascades. In this work, we focus on understanding and leveraging diffusion dynamics of false and legitimate contents in order to facilitate network interventions for fake news mitigation. We analyze real-world Twitter datasets comprising fake and true news cascades, to understand differences in diffusion dynamics and user behaviours with regards to fake and true contents. Based on the analysis, we model the diffusion as a mixture of Independent Cascade models (MIC) with parameters \theta_T , \theta_F over the social network graph; and derive unsupervised inference techniques for parameter estimation of the diffusion mixture model from observed, unlabeled cascades. Users influential in the propagation of true and fake contents are identified using the inferred diffusion dynamics. Characteristics of the identified influential users reveal positive correlation between influential users identified for fake news and their relative appearance in fake news cascades. Identified influential users tend to be related to topics of more viral information cascades than less viral ones; and identified fake news influential users have relatively fewer counts of direct followers, compared to the true news influential users. Intervention analysis on nodes and edges demonstrates capacity of the inferred diffusion dynamics in supporting network interventions for mitigation.
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Khan, Shakeel Ahmad, Khurram Shahzad, Omer Shabbir, and Abid Iqbal. "Developing a Framework for Fake News Diffusion Control (FNDC) on Digital Media (DM): A Systematic Review 2010–2022." Sustainability 14, no. 22 (November 17, 2022): 15287. http://dx.doi.org/10.3390/su142215287.
Повний текст джерелаАнотація:
This study sought to investigate factors causing the spread of fake news on digital media (DM) and to explore the sometimes disastrous consequences of fake news on social media. The study also aimed to construct a framework for fake news disaster management to control the dangers of false news on DM. The study applied PRISMA guidelines and techniques for exploring, devising, and inclusion and exclusion criteria. The search was carried out through 15 of the world’s leading digital databases. As a result, 31 peer-reviewed studies published in impact-factor journals of leading databases were included. Findings showed that several factors influenced the sharing of fake news on digital media (DM) platforms. Six major trending factors were the rise of technologies, social connections, political reasons, the absence of a controlling center, online business and marketing, and quick dissemination of information. The study identified the disadvantages of fake news (FN) on digital media (DM). A framework was constructed for managing fake news disasters to control the spread of fake news on digital media. This paper offers important theoretical contributions through the development of a framework for controlling fake news spread on digital media and by providing a valuable addition to the existing body of knowledge. The study offers practical assistance to top management, decision makers, and policymakers to devise policies to effectively manage problems caused by fake news dissemination. It provides practical strategies to address fake news disasters on digital media for redefining social values. This research also assists digital media managers in utilizing the proposed framework and controlling the harmful impact of fake news on social media.
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Huang, Haifeng, Serra Boranbay-Akan, and Ling Huang. "Media, Protest Diffusion, and Authoritarian Resilience." Political Science Research and Methods 7, no. 1 (June 13, 2016): 23–42. http://dx.doi.org/10.1017/psrm.2016.25.
Повний текст джерелаАнотація:
Do authoritarian governments always censor news about protests to prevent unrest from spreading? Existing research on authoritarian politics stresses the danger that information spread within the society poses for a regime. In particular, media and Internet reports of social unrest are deemed to threaten authoritarian rule, as such reports may incite more protests and thus spread instability. We show that such reasoning is incomplete if social protests are targeted at local officials. Allowing media the freedom to report local protests may indeed lead to protest diffusion, but the increased probability of citizen protest also has two potential benefits for the regime: (1) identifying and addressing more social grievances, thus releasing potential revolutionary pressure on the regime; (2) forcing local officials to reduce misbehavior, thus reducing underlying social grievances. For authoritarian governments whose survival is vulnerable to citizen grievances, allowing the media to report social protests aimed at local governments can therefore enhance regime stability and protect its interests under many circumstances. We construct a game-theoretic model to analyze the problem and illustrate the argument with examples from China.
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Ravenelle, Alexandrea J., Abigail Newell, and Ken Cai Kowalski. "“The Looming, Crazy Stalker Coronavirus”: Fear Mongering, Fake News, and the Diffusion of Distrust." Socius: Sociological Research for a Dynamic World 7 (January 2021): 237802312110247. http://dx.doi.org/10.1177/23780231211024776.
Повний текст джерелаАнотація:
The authors explore media distrust among a sample of precarious and gig workers interviewed during the COVID-19 pandemic. Although these left-leaning respondents initially increased their media consumption at the outset of the pandemic, they soon complained of media sensationalism and repurposed a readily available cultural tool: claims of “fake news.” As a result, these unsettled times have resulted in a “diffusion of distrust,” in which an elite conservative discourse of skepticism toward the media has also become a popular form of compensatory control among self-identified liberals. Perceiving “fake news” and media sensationalism as “not good” for their mental health, respondents also reported experiencing media burnout and withdrawing from media consumption. As the pandemic passes its one-year anniversary, this research has implications for long-term media coverage on COVID-19 and ongoing media trust and consumption.
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Tandoc, Edson C., Darren Lim, and Rich Ling. "Diffusion of disinformation: How social media users respond to fake news and why." Journalism 21, no. 3 (August 7, 2019): 381–98. http://dx.doi.org/10.1177/1464884919868325.
Повний текст джерелаАнотація:
This exploratory study seeks to understand the diffusion of disinformation by examining how social media users respond to fake news and why. Using a mixed-methods approach in an explanatory-sequential design, this study combines results from a national survey involving 2501 respondents with a series of in-depth interviews with 20 participants from the small but economically and technologically advanced nation of Singapore. This study finds that most social media users in Singapore just ignore the fake news posts they come across on social media. They would only offer corrections when the issue is strongly relevant to them and to people with whom they share a strong and close interpersonal relationship.
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P, Rupa. "Diffusion of News through Social Media with Reference to the Kiss of Love Movement on Facebook." Artha - Journal of Social Sciences 14, no. 3 (July 1, 2015): 22. http://dx.doi.org/10.12724/ajss.34.4.
Повний текст джерелаАнотація:
Web 2.0 is an interactive medium that has paved the way for the democratization of news. News is no longer the domain of the elite who disseminate it to the masses via mass media. In the age of Web 2.0, where social media rules the roost, every individual is a content generator and a purveyor of information. News is now more viral than ever. Facebook with over a hundred million users in India is the most popular social networking site in India. People use Facebook to connect, like, share and comment on everything from politics to culture to religion and so on. They also use it to disseminate news that they connect with on a personal level; along with their opinions on the same. This way, they become creators and transmitters of information.The Kiss of Love movement is a non-violent protest against moral policing which began when a Facebook page called 'Kiss of love' asked the youth across Kerala to participate in a protest against moral policing on 2nd November, 2014, at Marine Drive, Cochin. The controversial movement has snowballed into a mass movement which has spread into other states. A campaign of this magnitude has been made possible due to viral diffusion of news, information and comments on Facebook. This study uses quantitative and qualitative tools to study the diffusion of news with regard to the Kiss of Love movement through Facebook in an attempt to shed more light on the diffusion process of information through social media.
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Khalifa, Hussein Khalifa Hassan, Mujeeb Saif Mohsen Al-Absy, Siddig Balal Ibrahim, Abdallah Abdelrahim Mohamed Mahmoud Moawad, Abdulla Ibrahim Al-Taher, and Ahmad Al Tawalbeh. "COVID-19 and Diffusion of Rumors among Arab Social Media Users: Reasons and Solutions." Journal of Hunan University Natural Sciences 49, no. 6 (June 30, 2022): 199–208. http://dx.doi.org/10.55463/issn.1674-2974.49.6.20.
Повний текст джерелаАнотація:
As the major source of information, social media has outpaced mainstream news channels. In these information overloaded days, differentiating rumors from facts is crucial and difficult. This study aims to explore the respondents’ perception of the reasons for spreading rumors related to the Coronavirus on social media. Furthermore, it seeks to look at the respondents’ perception of the ways of combating fake news related to the Coronavirus on social media. Lastly, the study attempts to know to what extent the respondents are satisfied with the performance of the media institutions in dealing with the Coronavirus. A cross-sectional survey design was used with a non-probability sample to explore the respondents’ perceptions of the above-mentioned aims. A total of 1274 self-selected cases from Bahrain, Egypt, Iraq, Jordan, Morocco, Oman, Saudi Arabia, Sudan, and the United Arab Emirates were investigated. The study finds that all respondents agree with the reasons listed in the survey about spreading rumors related to the Coronavirus on social media, except for the reasons of lacking transparency on behalf of the Ministry of Health and other official bodies and lacking accurate information about the Coronavirus. Moreover, the study confirms the respondents’ beliefs that all listed ways effectively combat fake news related to the Coronavirus on social media. Furthermore, the study finds that the respondents are satisfied with the performance of the media institutions in dealing with the Coronavirus in their countries. With these findings, the study significantly contributes to the literature. It may assist various parties, such as the government and media organizations, in making the proper decision to combat the spread of rumors via social media.
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Welbers, Kasper, and Michaël Opgenhaffen. "Social media gatekeeping: An analysis of the gatekeeping influence of newspapers’ public Facebook pages." New Media & Society 20, no. 12 (July 11, 2018): 4728–47. http://dx.doi.org/10.1177/1461444818784302.
Повний текст джерелаАнотація:
Due to the rising importance of social media platforms for news diffusion, newspapers are relying on social media editors to promote the distribution of their news items on these platforms. In this study, we investigate how much of an impact these social media editors really have, focusing on the impact of newspapers’ public pages on Facebook. Since the actions of individual users are not visible on many platforms due to privacy consideration, we propose a method that leverages time series of aggregated scores for total user engagement, which are available for various platforms. We use this method to study and compare the influence of Facebook pages for six newspapers from the United Kingdom, the Netherlands, and Flanders, for all news items published over 2 weeks in 2017.
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Sun, Eric, Itamar Rosenn, Cameron Marlow, and Thomas Lento. "Gesundheit! Modeling Contagion through Facebook News Feed." Proceedings of the International AAAI Conference on Web and Social Media 3, no. 1 (March 19, 2009): 146–53. http://dx.doi.org/10.1609/icwsm.v3i1.13947.
Повний текст джерелаАнотація:
Whether they are modeling bookmarking behavior in Flickr or cascades of failure in large networks, models of diffusion often start with the assumption that a few nodes start long chain reactions, resulting in large-scale cascades. While reasonable under some conditions, this assumption may not hold for social media networks, where user engagement is high and information may enter a system from multiple disconnected sources. Using a dataset of 262,985 Facebook Pages and their associated fans, this paper provides an empirical investigation of diffusion through a large social media network. Although Facebook diffusion chains are often extremely long (chains of up to 82 levels have been observed), they are not usually the result of a single chain-reaction event. Rather, these diffusion chains are typically started by a substantial number of users. Large clusters emerge when hundreds or even thousands of short diffusion chains merge together. This paper presents an analysis of these diffusion chains using zero-inflated negative binomial regressions. We show that after controlling for distribution effects, there is no meaningful evidence that a start node’s maximum diffusion chain length can be predicted with the user's demographics or Facebook usage characteristics (including the user's number of Facebook friends). This may provide insight into future research on public opinion formation.
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Chen, Jinyin, Chengyu Jia, Qinfeng Li, Haibin Zheng, Wenhong Zhao, Mingyuan Yan, and Changting Lin. "Research on Fake News Detection Based on Diffusion Growth Rate." Wireless Communications and Mobile Computing 2022 (July 14, 2022): 1–11. http://dx.doi.org/10.1155/2022/6329014.
Повний текст джерелаАнотація:
With the rapid development of the Internet, social media has become a convenient online platform for users to obtain information, express opinions, and communicate with each other. Users are keen to participate in discussions on hot topics and exchange opinions on social media. A lot of fake news has also arisen at this moment. However, existing fake news detection methods have the problem of relying too much on textual features. Textual features are easy to be tampered with and deceive the detector; thus, it is difficult to distinguish fake news only by relying on textual features. To address the challenge, we propose a fake news detection method based on the diffusion growth rate (Delta-G). To identify the real and fake news, Delta-G uses graph convolutional networks to extract the diffusion structure features and then adopts the long-short-term memory networks to extract the growth rate features on time series. In the experiments, Delta-G is verified on two news datasets, Twitter and Weibo. Compared with the three detection methods of decision tree classifier, support vector machines with a propagation tree kernel, and RvNN, the accuracy of the Delta-G on the two datasets is improved by an average of 5% or more, which is better than all the baselines.
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Tuten, Tracy, and Victor Perotti. "Lies, brands and social media." Qualitative Market Research: An International Journal 22, no. 1 (January 14, 2019): 5–13. http://dx.doi.org/10.1108/qmr-02-2017-0063.
Повний текст джерелаАнотація:
Purpose The purpose of this study is to illustrate the influence of media coverage and sentiment about brands on user-generated content amplification and opinions expressed in social media. Design/methodology/approach This study used a mixed-method approach, using a brand situation as a case example, including sentiment analysis of social media conversations and sentiment analysis of media coverage. This study tracks the diffusion of a false claim about the brand via online media coverage, subsequent spreading of the false claim via social media and the resulting impact on sentiment toward the brand. Findings The findings illustrate the influence of digital mass communication sources on the subsequent spread of information about a brand via social media channels and the impact of the social spread of false claims on brand sentiment. This study illustrates the value of social media listening and sentiment analysis for brands as an ongoing business practice. Research limitations/implications While it has long been known that media coverage is in part subsequently diffused through individual sharing, this study reveals the potential for media sentiment to influence sentiment toward a brand. It also illustrates the potential harm brands face when false information is spread via media coverage and subsequently through social media posts and conversations. How brands can most effectively correct false brand beliefs and recover from negative sentiment related to false claims is an area for future research. Practical implications This study suggests that brands are wise to use sentiment analysis as part of their evaluation of earned media coverage from news organizations and to use social listening as an alert system and sentiment analysis to assess impact on attitudes toward the brand. These steps should become part of a brand’s social media management process. Social implications Media are presumed to be impartial reporters of news and information. However, this study illustrated that the sentiment expressed in media coverage about a brand can be measured and diffused beyond the publications’ initial reach via social media. Advertising positioned as news must be labeled as “advertorial” to ensure that those exposed to the message understand that the message is not impartial. News organizations may inadvertently publish false claims and relay information with sentiment that is then carried via social media along with the information itself. Negative information about a brand may be more sensational and, thus, prone to social sharing, no matter how well the findings are researched or sourced. Originality/value The value of the study is its illustration of how false information and media sentiment spread via social media can ultimately affect consumer sentiment and attitude toward the brand. This study also explains the research process for social scraping and sentiment analysis.
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Surette, Ray. "A copycat crime meme: Ghost riding the whip." Crime, Media, Culture: An International Journal 16, no. 2 (August 1, 2019): 239–64. http://dx.doi.org/10.1177/1741659019865305.
Повний текст джерелаАнотація:
A 2006 US copycat crime wave came into being, surged with thousands of crimes committed, and dissipated without substantial news media attention. The development of this early copycat crime meme is traceable to the nature of the crime, “ghost riding the whip,” and the social media and popular music communication channels associated with it. Ghost riding the whip involved traffic violations where drivers exit their cars and dance atop or alongside the moving driverless vehicles. Social media allowed the widespread diffusion of detailed instructions that spread this crime from a single minority community to the middle class within a 3-month period. The study of this copycat crime meme examined four types of data: Google Trends, rap songs, ProQuest news media data, and YouTube videos. The examination of the crime wave suggests how Gabriel Tarde’s 19th-century ideas operate in the contemporary social media era. However, unlike pre-social media-based crime waves that were launched via interpersonal and legacy media communication pathways, for ghost riding, rap songs, YouTube postings, and Google searches spurred its growth. Legacy media were found to be most important during the crime wave’s decline, but not during its diffusion. For this copycat crime meme, social media’s influence flowed in a unique upward and outward pattern and the results raise the research questions as to how social media have changed the dynamics of crime waves and how important legacy media will be in future crime waves.
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Cinelli, Matteo, Gianmarco De Francisci Morales, Alessandro Galeazzi, Walter Quattrociocchi, and Michele Starnini. "The echo chamber effect on social media." Proceedings of the National Academy of Sciences 118, no. 9 (February 23, 2021): e2023301118. http://dx.doi.org/10.1073/pnas.2023301118.
Повний текст джерелаАнотація:
Social media may limit the exposure to diverse perspectives and favor the formation of groups of like-minded users framing and reinforcing a shared narrative, that is, echo chambers. However, the interaction paradigms among users and feed algorithms greatly vary across social media platforms. This paper explores the key differences between the main social media platforms and how they are likely to influence information spreading and echo chambers’ formation. We perform a comparative analysis of more than 100 million pieces of content concerning several controversial topics (e.g., gun control, vaccination, abortion) from Gab, Facebook, Reddit, and Twitter. We quantify echo chambers over social media by two main ingredients: 1) homophily in the interaction networks and 2) bias in the information diffusion toward like-minded peers. Our results show that the aggregation of users in homophilic clusters dominate online interactions on Facebook and Twitter. We conclude the paper by directly comparing news consumption on Facebook and Reddit, finding higher segregation on Facebook.
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Magolanga, Elisha, Andindilile Michael, and Fatuma Simba. "The Use of Online Social Media Platforms by Tanzania Journalists to Produce and Disseminate Development News." Tanzania Journal of Engineering and Technology 41, no. 3 (December 11, 2022): 38–50. http://dx.doi.org/10.52339/tjet.v41i3.843.
Повний текст джерелаАнотація:
Advancement in Information and Communication Technologies (ICT) are revolutionizing Development Journalism (DJ) into an effective strategy for building competitive economies globally. However, many countries in Africa including Tanzania are slow in tapping potentials of ICT for development journalism. Digital penetration in the form of the Internet and social media are changing the way in which journalists are mobilizing and engaging communities in journalism practises, a key strategy for a competitive digitalised economy. This paper was guided by Development Communication Theory and Diffusion of Innovation Theory to explore the use of online social media by journalists in Tanzania to practice development journalism. Combining quantitative and qualitative methods, this paper analyses risks and benefits of online social media platforms in contributing to national development goals. A total of 15 social media journalists and managers from Mwananchi Digital, Ayo TV and Azam TV gave their insights in this paper. Use of online social media platforms for development news is still in its infancy stage, key findings show that out of 270 (100%) studied news items, only 66 (24%) manifested development news practice. Non-development news accounted for 204 (76%) of news items analysed. There were only four (4) (27%) journalists who use social media as a source of information, and platform for disseminating development news in Ayo TV compared to 3 (20%) in Mwananchi Digital and 5 (33%) in Azam TV. Results call upon some intensive national efforts to empower journalists in the use of ICT and mobilize audience in the changing communication patterns.
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Angioli, Roberto, Massimo Casciello, Salvatore Lopez, Francesco Plotti, Lidia Di Minco, Paola Frati, Vittorio Fineschi, et al. "Assessing HPV vaccination perceptions with online social media in Italy." International Journal of Gynecologic Cancer 29, no. 3 (January 10, 2019): 453–58. http://dx.doi.org/10.1136/ijgc-2018-000079.
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ObjectiveBecause of the widespread availability of the internet and social media, people often collect and disseminate news online making it important to understand the underlying mechanisms to steer promotional strategies in healthcare. The aim of this study is to analyze perceptions regarding the human papillomavirus (HPV) vaccine in Italy.MethodsFrom August 2015 to July 2016, articles, news, posts, and tweets were collected from social networks, posts on forums, blogs, and pictures about HPV. Using other keywords and specific semantic rules, we selected conversations presenting the negative or positive perceptions of HPV. We divided them into subgroups depending on the website, publication date, authors, main theme, and transmission modality.ResultsMost conversations occurred on social networks. Of all the conversations regarding HPV, more than 50% were about vaccination. With regard to conversations exclusively on the HPV vaccine, 47%, 32%, and 21% were positive, negative and neutral, respectively. Only 9% of the conversations mentioned the vaccine trade name and, in these conversations, perception was almost always negative. We observed many peaks in positive conversation trends compared with negative trends. The peaks were related to the web dissemination of particular news regarding HPV vaccination.ConclusionsIn this study we have shown how mass media influences the diffusion of both negative and positive perceptions about HPV vaccines and suggest better ways to inform people about the importance of HPV vaccination.
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SÜTÇÜ, CEM, and SERKAN BAYRAKÇI. "HOW DO SOCIAL MEDIA AFFECT NEWSPAPERS? A RESEARCH ON DIFFUSION OF NEWS ON TWIITER." Turkish Online Journal of Design, Art and Communication 4, no. 2 (April 1, 2014): 40–52. http://dx.doi.org/10.7456/10402100/003.
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Zhang, Yini, Josephine Lukito, Min-Hsin Su, Jiyoun Suk, Yiping Xia, Sang Jung Kim, Larissa Doroshenko, and Chris Wells. "Assembling the Networks and Audiences of Disinformation: How Successful Russian IRA Twitter Accounts Built Their Followings, 2015–2017." Journal of Communication 71, no. 2 (April 1, 2021): 305–31. http://dx.doi.org/10.1093/joc/jqaa042.
Повний текст джерелаАнотація:
Abstract This study investigates how successful Russian Internet Research Agency (IRA) Twitter accounts constructed the followings that were central to their disinformation campaigns around the 2016 U.S. presidential election. Treating an account’s social media following as both an ego network and an audience critical for information diffusion and influence accrual, we situate IRA Twitter accounts’ accumulation of followers in the ideologically polarized, attention driven, and asymmetric political communication system. Results show that partisan enclaves on Twitter contributed to IRA accounts’ followings through retweeting; and that mainstream and hyperpartisan media assisted conservative IRA accounts’ following gain by embedding their tweets in news. These results illustrate how network dynamics within social media and news media amplification beyond it together boosted social media followings. Our results also highlight the dynamics fanning the flames of disinformation: partisan polarization, media fragmentation and asymmetry, and an attention economy optimized for engagement rather than accuracy.
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Hapsari, Dian Tri. "INOVASI JURNALIS DARING DALAM KOLABORASI TIM CEK FAKTA SELAMA PEMBERITAAN PILPRES 2019." Interaksi: Jurnal Ilmu Komunikasi 9, no. 1 (May 22, 2020): 51–63. http://dx.doi.org/10.14710/interaksi.9.1.51-63.
Повний текст джерелаАнотація:
The development of information through social media gave birth to the phenomenon of decrease quality information with easy spread of hoaxes. This research analyzes the formation collaboration of the fact check team from several elements of the media community such as the Indonesian online Media Association (AMSI), the Alliance of Independent Journalists (AJI), the Indonesian Anti-Defamation Society (Mafindo), and 24 mainstream online medias in Indonesia during the 2019 Presidential Election news. This reserch argues that the collaboration is a diffusion of innovation over the changing digital culture system of Indonesian society. Seeing this phenomenon, this article argues the importance of collaborative innovation among elements of mass media in strengthening mass media institutions as a gatekeeper for reliable information sources and media literacy amid the rapid development of digital news and newsrooms. This qualitative research aims to remind the importance of media activists to continue collaborating in innovation digital space such as the fact check team activities to increase media literacy in the disruption era. The conclusion of this research states that process diffusion of innovation of collaborating fact checking by the media community has changed journalist work to do high quality logic of journalism. Media innovation that promotes data journalism is a main foundation for combating hoax in the digital media in order to establish democratic public sphare.
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Kim, Minkyoung, and Soohwan Kim. "Dynamics of macroscopic diffusion across meta-populations with top-down and bottom-up approaches: A review." Mathematical Biosciences and Engineering 19, no. 5 (2022): 4610–26. http://dx.doi.org/10.3934/mbe.2022213.
Повний текст джерелаАнотація:
<abstract><p>Human interaction patterns on the Web over online social networks vary with the context of communication items (<italic>e.g.,</italic> politics, economics, disasters, celebrities, and etc.), which leads to form unlimited time-evolving curves of information adoption as diffusion proceeds. Online communications often continue to navigate through heterogeneous social systems consisting of a wide range of online media such as social networking sites, blogs, and mainstream news. This makes it very challenging to uncover the underlying causal mechanisms of such macroscopic diffusion. In this respect, we review both top-down and bottom-up approaches to understand the underlying dynamics of an individual item's popularity growth across multiple meta-populations in a complementary way. For a case study, we use a dataset consisting of time-series adopters for over 60 news topics through different online communication channels on the Web. In order to find disparate patterns of macroscopic information propagation, we first generate and cluster the diffusion curves for each target meta-population and then estimate them with two different and complementary approaches in terms of the strength and directionality of influences across the meta-populations. In terms of the strength of influence, we find that synchronous global diffusion is not possible without very strong intra-influence on each population. In terms of the directionality of influence between populations, such concurrent propagation is likely brought by transitive relations among heterogeneous populations. When it comes to social context, controversial news topics in politics and human culture (<italic>e.g.,</italic> political protests, multiculturalism failure) tend to trigger more synchronous than asynchronous diffusion patterns across different social media on the Web. We expect that this study can help to understand dynamics of macroscopic diffusion across complex systems in diverse application domains.</p></abstract>
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RODRIGUEZ, MANUEL GOMEZ, JURE LESKOVEC, DAVID BALDUZZI, and BERNHARD SCHÖLKOPF. "Uncovering the structure and temporal dynamics of information propagation." Network Science 2, no. 1 (April 2014): 26–65. http://dx.doi.org/10.1017/nws.2014.3.
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AbstractTime plays an essential role in the diffusion of information, influence, and disease over networks. In many cases we can only observe when a node is activated by a contagion—when a node learns about a piece of information, makes a decision, adopts a new behavior, or becomes infected with a disease. However, the underlying network connectivity and transmission rates between nodes are unknown. Inferring the underlying diffusion dynamics is important because it leads to new insights and enables forecasting, as well as influencing or containing information propagation. In this paper we model diffusion as a continuous temporal process occurring at different rates over a latent, unobserved network that may change over time. Given information diffusion data, we infer the edges and dynamics of the underlying network. Our model naturally imposes sparse solutions and requires no parameter tuning. We develop an efficient inference algorithm that uses stochastic convex optimization to compute online estimates of the edges and transmission rates. We evaluate our method by tracking information diffusion among 3.3 million mainstream media sites and blogs, and experiment with more than 179 million different instances of information spreading over the network in a one-year period. We apply our network inference algorithm to the top 5,000 media sites and blogs and report several interesting observations. First, information pathways for general recurrent topics are more stable across time than for on-going news events. Second, clusters of news media sites and blogs often emerge and vanish in a matter of days for on-going news events. Finally, major events, for example, large scale civil unrest as in the Libyan civil war or Syrian uprising, increase the number of information pathways among blogs, and also increase the network centrality of blogs and social media sites.
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Ferrara, Emilio, and Zeyao Yang. "Quantifying the effect of sentiment on information diffusion in social media." PeerJ Computer Science 1 (September 30, 2015): e26. http://dx.doi.org/10.7717/peerj-cs.26.
Повний текст джерелаАнотація:
Social media has become the main vehicle of information production and consumption online. Millions of users every day log on their Facebook or Twitter accounts to get updates and news, read about their topics of interest, and become exposed to new opportunities and interactions. Although recent studies suggest that the contents users produce will affect the emotions of their readers, we still lack a rigorous understanding of the role and effects of contents sentiment on the dynamics of information diffusion. This work aims at quantifying the effect of sentiment on information diffusion, to understand: (i) whether positive conversations spread faster and/or broader than negative ones (or vice-versa); (ii) what kind of emotions are more typical of popular conversations on social media; and, (iii) what type of sentiment is expressed in conversations characterized by different temporal dynamics. Our findings show that, at the level of contents, negative messages spread faster than positive ones, but positive ones reach larger audiences, suggesting that people are more inclined to share and favorite positive contents, the so-calledpositive bias. As for the entire conversations, we highlight how different temporal dynamics exhibit different sentiment patterns: for example, positive sentiment builds up for highly-anticipated events, while unexpected events are mainly characterized by negative sentiment. Our contribution represents a step forward to understand how the emotions expressed in short texts correlate with their spreading in online social ecosystems, and may help to craft effective policies and strategies for content generation and diffusion.
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Ihm, Jennifer, and Eun-mee Kim. "The hidden side of news diffusion: Understanding online news sharing as an interpersonal behavior." New Media & Society 20, no. 11 (May 11, 2018): 4346–65. http://dx.doi.org/10.1177/1461444818772847.
Повний текст джерелаАнотація:
Unlike previous approaches to online news sharing behaviors dealing with the dissemination of information to broader audiences, this article interprets that behavior as an act of relational communication. Drawing from surveys of 400 online news users, we examine how they manage their self-presentation and account for their audience’s characteristics differently when they engage in online news sharing activities on mobile instant messenger (MIM) and social networking site (SNS). Our findings suggest that individuals who are highly motivated by self-presentation share news online more than others. Individuals also target different audiences, depending on their media environments. Specifically, SNS users are more cautious about their audiences’ connections with other users. The implications are that news sharing behaviors are a type of communication used for forming relationships and managing impressions beyond informational purposes, based on active individuals’ strategic considerations of their audiences’ characteristics.
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Karyotakis, Minos-Athanasios, Evangelos Lamprou, Matina Kiourexidou, and Nikos Antonopoulos. "SEO Practices: A Study about the Way News Websites Allow the Users to Comment on Their News Articles." Future Internet 11, no. 9 (August 30, 2019): 188. http://dx.doi.org/10.3390/fi11090188.
Повний текст джерелаАнотація:
In the current media world, there is a huge debate about the importance of the visibility of a news website in order to secure its existence. Thus, search engine optimization (SEO) practices have emerged in the news media systems around the world. This study aimed to expand the current literature about the SEO practices by focusing on examining, via the walkthrough method, the ways that news companies allow the users to comment on their online news articles. The comments on the news websites are related to the notions of social influence, information diffusion, and play an essential role as a SEO practice, for instance, by providing content and engagement. The examined sample was collected by the most visited news websites’ rankings of alexa.com for a global scale and for the countries Greece and Cyprus. The findings reveal that the news websites throughout the globe use similar features and ways to support the comments of the users. In the meantime, though, a high number of the news websites did not allow the users to use their social media accounts in order to comment the provided news articles, or provided multiple comment platforms. This trend goes against the SEO practices. It is believed that this finding is associated with the difficulty of the news organizations to regulate and protect themselves by the users’ comments that promote, in some case harmful rhetoric and polarization.
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Zhou, Nan, Xiu-Xiu Zhan, Song Lin, Shang-Hui Yang, Chuang Liu, Gui-Quan Sun, and Zi-Ke Zhang. "Information diffusion on communication networks based on Big Data analysis." Electronic Library 35, no. 4 (August 7, 2017): 745–57. http://dx.doi.org/10.1108/el-09-2016-0194.
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Purpose Information carriers (including mass media and We-Media) play important roles in information diffusion on social networks. The purpose of this paper is to investigate changes in the dissemination of information combing with data analysis. Design/methodology/approach This work analyzed nearly 200 years of coverage of different information carriers during different periods of human society, from the period of only mouth-to-mouth communication to the period of modern society. Information diffusion models are built to illustrate how the information dynamic changes with time and combined box office data of several movies to predict the process of information diffusion. In addition, a metric is defined to identify which information would become news in the future. Findings Results show that with the development of information carriers, information spreads faster and wider nowadays. The correctness of the metric proposed has been validated. Research limitations/implications The structure of social networks influences the dissemination of information. There are an enormous number of factors that influence the formation of hotspots. Practical implications The results and conclusion of this work will benefit by predicting the evolution of information carriers. The metric proposed will aid in searching hot news in the future. Originality/value This work may shed some light on a better understanding of information diffusion, spreading not only on social networks but also on the carriers used for the information spreading.
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Orozco Macías, Andrés Fernando. "The fall of Gaddafi through CNN and Fox News." ÁNFORA 26, no. 46 (December 12, 2018): 1–10. http://dx.doi.org/10.30854/anf.v26.n46.2019.550.
Повний текст джерелаАнотація:
Objectives: to determine the media construction criteria on Gaddafi as a political enemy based on Fox News and CNN news discourse analysis regarding his fall from power.
Methodology: a qualitative method and a content analysis was applied which contrasted the Fox and CNN channels’ news concepts and images. Based on this, a logical-sequential speech classification was elaborated under the Edelman Murray theoretical model.
Results: it was found that the news around the fall of Gaddafi had been manipulated in texts and images in in a way of framing the narration with predetermined interests. In addition, it was evident that television media has a high degree of political influence in that it can suggest to the viewer that some political deaths must be celebrated while there are others that must be reproached as long as three sequences are fulfilled: the social problems creation, the justification, and the enemies construction.
Conclusions: television channels with high global diffusion and political interests indicate what social problems are necessary to highlight in the media. Ignorance about the political and social processes in Libya lead the Fox and CNN audiences to accept, without prior or later questioning, the actions of NATO in Libya.
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Fang, Youjia, Xin Chen, Zheng Song, Tianzi Wang, and Yang Cao. "Modelling Propagation of Public Opinions on Microblogging Big Data Using Sentiment Analysis and Compartmental Models." International Journal on Semantic Web and Information Systems 13, no. 1 (January 2017): 11–27. http://dx.doi.org/10.4018/ijswis.2017010102.
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Compartmental models have been used to model information diffusion on social media. However, there have been few studies on modelling positive and negative public opinions using compartmental models. This study aimed for using sentiment analysis and compartmental model to model the propagation of positive and negative opinions on microblogging big media. The authors studied the news propagation of seven popular social topics on China's Sina Weibo microblogging platform. Natural language processing and sentiment analysis were used to identify public opinions from microblogging big data. Then two existing (SIZ and SEIZ) models and a newly developed (SE2IZ) model were implemented to model the news propagation and evaluate the trends of public opinions on selected social topics. Simulation study was used to check model fitting performance. The results show that the new SE2IZ model has a better model fitting performance than existing models. This study sheds some new light on using social media for public opinion estimation and prediction.
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Mitchelstein, Eugenia, Mora Matassi, and Pablo J. Boczkowski. "Minimal Effects, Maximum Panic: Social Media and Democracy in Latin America." Social Media + Society 6, no. 4 (October 2020): 205630512098445. http://dx.doi.org/10.1177/2056305120984452.
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In face of public discourses about the negative effects that social media might have on democracy in Latin America, this article provides a qualitative assessment of existing scholarship about the uses, actors, and effects of platforms for democratic life. Our findings suggest that, first, campaigning, collective action, and electronic government are the main political uses of platforms. Second, politicians and office holders, social movements, news producers, and citizens are the main actors who utilize them for political purposes. Third, there are two main positive effects of these platforms for the democratic process—enabling social engagement and information diffusion—and two main negative ones—the presence of disinformation, and the spread of extremism and hate speech. A common denominator across positive and negative effects is that platforms appear to have minimal effects that amplify pre-existing patterns rather than create them de novo.
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Mu, Yida, and Nikolaos Aletras. "Identifying Twitter users who repost unreliable news sources with linguistic information." PeerJ Computer Science 6 (December 14, 2020): e325. http://dx.doi.org/10.7717/peerj-cs.325.
Повний текст джерелаАнотація:
Social media has become a popular source for online news consumption with millions of users worldwide. However, it has become a primary platform for spreading disinformation with severe societal implications. Automatically identifying social media users that are likely to propagate posts from handles of unreliable news sources sometime in the future is of utmost importance for early detection and prevention of disinformation diffusion in a network, and has yet to be explored. To that end, we present a novel task for predicting whether a user will repost content from Twitter handles of unreliable news sources by leveraging linguistic information from the user’s own posts. We develop a new dataset of approximately 6.2K Twitter users mapped into two categories: (1) those that have reposted content from unreliable news sources; and (2) those that repost content only from reliable sources. For our task, we evaluate a battery of supervised machine learning models as well as state-of-the-art neural models, achieving up to 79.7 macro F1. In addition, our linguistic feature analysis uncovers differences in language use and style between the two user categories.
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Savila, Fa’asisila, Anele Bamber, Sandra Smith, Karen V. Fernandez, Truely Harding, Dave Letele, Bert van der Werf, et al. "Process evaluation of in-person, news and social media engagement of a community-based programme Brown Buttabean Motivation (BBM): a research protocol." BMJ Open 12, no. 11 (November 2022): e062092. http://dx.doi.org/10.1136/bmjopen-2022-062092.
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IntroductionThe community group Brown Buttabean Motivation (BBM) initially began to assist Auckland Pasifika and Māori to manage weight problems, predominantly through community-based exercise sessions and social support. BBM’s activities expanded over time to include many other components of healthy living in response to community need. With advent of the COVID-19 pandemic, BBM outreach grew to include a foodbank distributing an increasing amount of donated healthy food to families in need, a community kitchen and influenza and COVID-19 vaccine drives. A strong social media presence has served as the main means of communication with the BBM community as well as use of traditional news media (written, radio, television) to further engage with vulnerable members of the community.Methods and analysisThe study aims to conduct mixed method process evaluation of BBM’s community engagement through in-person, social and news media outreach activities with respect to the health and well-being of Pasifika and Māori over time. The project is informed by theoretical constructs including Pacific Fa’afaletui and Fonofale and Māori Te Whare Tapa Whā Māori research frameworks and principles of Kaupapa Māori. It is further framed using the concept of community-driven diffusion of knowledge and engagement through social networks. Data sources include in-person community engagement databases, social and news media outreach data from archived documents and online resources. Empirical data will undergo longitudinal and time series statistical analyses. Qualitative text thematic analyses will be conducted using the software NVivo, Leximancer and AntConc. Image and video visual data will be randomly sampled from two social media platforms. The social media dataset contains almost 8000 visual artefacts.Ethics and disseminationEthics approval obtained from University of Auckland Human Participants Ethics Committee UAHPEC 23456. Findings will be published in peer-reviewed publications, disseminated through community meetings and conferences and via BBM social network platforms.Trial registration numberACTRN 12621 00093 1875
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González-Bailón, Sandra, and Manlio De Domenico. "Bots are less central than verified accounts during contentious political events." Proceedings of the National Academy of Sciences 118, no. 11 (March 8, 2021): e2013443118. http://dx.doi.org/10.1073/pnas.2013443118.
Повний текст джерелаАнотація:
Information manipulation is widespread in today’s media environment. Online networks have disrupted the gatekeeping role of traditional media by allowing various actors to influence the public agenda; they have also allowed automated accounts (or bots) to blend with human activity in the flow of information. Here, we assess the impact that bots had on the dissemination of content during two contentious political events that evolved in real time on social media. We focus on events of heightened political tension because they are particularly susceptible to information campaigns designed to mislead or exacerbate conflict. We compare the visibility of bots with human accounts, verified accounts, and mainstream news outlets. Our analyses combine millions of posts from a popular microblogging platform with web-tracking data collected from two different countries and timeframes. We employ tools from network science, natural language processing, and machine learning to analyze the diffusion structure, the content of the messages diffused, and the actors behind those messages as the political events unfolded. We show that verified accounts are significantly more visible than unverified bots in the coverage of the events but also that bots attract more attention than human accounts. Our findings highlight that social media and the web are very different news ecosystems in terms of prevalent news sources and that both humans and bots contribute to generate discrepancy in news visibility with their activity.
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Zhang, Yaming, Fei Liu, Yaya H. Koura, and Yanyuan Su. "Dynamics of a Delayed Interactive Model Applied to Information Dissemination in Social Networks." Mathematical Problems in Engineering 2021 (March 8, 2021): 1–12. http://dx.doi.org/10.1155/2021/6611168.
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Reducing fake news and rumor propagation through social media may be challenging to achieve when dealing with sensible contents and communities with free access to online shared resources. Controlling rumor dissemination and promoting true news are the main techniques used to strangle false information that may result in dramatic effect on human wellbeing in an open or closed environment. In this article, we studied a predator-prey model with constant delay in both predator and prey equations and applied the proposed model to the underlying relationship between the existing rumor propagating through social media and the related authoritative information containing the truth broadcast to reduce the respective rumor negative effect on the targeted community. We showed that the proposed system was very responsive to small perturbations and exhibited complex dynamical behavior around the steady-state equilibrium when interaction occurs and delay is applied, considering the controlled situations. Numerical results suggested applying relatively small delay, which represents the ideal time to publish the related propagating rumor curative content to reduce its diffusion speed and promote the truth.
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Agarwal, Pushkal, Kiran Garimella, Sagar Joglekar, Nishanth Sastry, and Gareth Tyson. "Characterising User Content on a Multi-Lingual Social Network." Proceedings of the International AAAI Conference on Web and Social Media 14 (May 26, 2020): 2–11. http://dx.doi.org/10.1609/icwsm.v14i1.7274.
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Social media has been on the vanguard of political information diffusion in the 21st century. Most studies that look into disinformation, political influence and fake-news focus on mainstream social media platforms. This has inevitably made English an important factor in our current understanding of political activity on social media. As a result, there has only been a limited number of studies into a large portion of the world, including the largest, multilingual and multicultural democracy: India. In this paper we present our characterisation of a multilingual social network in India called ShareChat. We collect an exhaustive dataset across 72 weeks before and during the Indian general elections of 2019, across 14 languages. We investigate the cross lingual dynamics by clustering visually similar images together, and exploring how they move across language barriers. We find that Telugu, Malayalam, Tamil and Kannada languages tend to be dominant in soliciting political images (often referred to as memes), and posts from Hindi have the largest cross-lingual diffusion across ShareChat (as well as images containing text in English). In the case of images containing text that cross language barriers, we see that language translation is used to widen the accessibility. That said, we find cases where the same image is associated with very different text (and therefore meanings). This initial characterisation paves the way for more advanced pipelines to understand the dynamics of fake and political content in a multi-lingual and non-textual setting.
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De Choudhury, Munmun, Yu-Ru Lin, Hari Sundaram, Kasim Selcuk Candan, Lexing Xie, and Aisling Kelliher. "How Does the Data Sampling Strategy Impact the Discovery of Information Diffusion in Social Media?" Proceedings of the International AAAI Conference on Web and Social Media 4, no. 1 (May 16, 2010): 34–41. http://dx.doi.org/10.1609/icwsm.v4i1.14024.
Повний текст джерелаАнотація:
Platforms such as Twitter have provided researchers with ample opportunities to analytically study social phenomena. There are however, significant computational challenges due to the enormous rate of production of new information: researchers are therefore, often forced to analyze a judiciously selected “sample” of the data. Like other social media phenomena, information diffusion is a social process–it is affected by user context, and topic, in addition to the graph topology. This paper studies the impact of different attribute and topology based sampling strategies on the discovery of an important social media phenomena–information diffusion.We examine several widely-adopted sampling methods that select nodes based on attribute (random, location, and activity) and topology (forest fire) as well as study the impact of attribute based seed selection on topology based sampling. Then we develop a series of metrics for evaluating the quality of the sample, based on user activity (e.g. volume, number of seeds), topological (e.g. reach, spread) and temporal characteristics (e.g. rate). We additionally correlate the diffusion volume metric with two external variables–search and news trends. Our experiments reveal that for small sample sizes (30%), a sample that incorporates both topology and user context (e.g. location, activity) can improve on naive methods by a significant margin of ~15-20%.
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Rowe, Matthew, and Hassan Saif. "Mining Pro-ISIS Radicalisation Signals from Social Media Users." Proceedings of the International AAAI Conference on Web and Social Media 10, no. 1 (August 4, 2021): 329–38. http://dx.doi.org/10.1609/icwsm.v10i1.14716.
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The emergence and actions of the so-called Islamic State of Iraq and the Levant (ISIL/ISIS) has received widespread news coverage across the World, largely due to their capture of large swathes of land across Syria and Iraq, and the publishing of execution and propaganda videos. Enticed by such material published on social media and attracted to the cause of ISIS, there have been numerous reports of individuals from European countries (the United Kingdom and France in particular) moving to Syria and joining ISIS. In this paper our aim to understand what happens to Europe-based Twitter users before, during, and after they exhibit pro-ISIS behaviour (i.e. using pro-ISIS terms, sharing content from pro-ISIS accounts), characterising such behaviour as radicalisation signals. We adopt a data-mining oriented approach to computationally determine time points of activation (i.e. when users begin to adopt pro-ISIS behaviour), characterise divergent behaviour (both lexically and socially), and quantify influence dynamics as pro-ISIS terms are adopted. Our findings show that: (i) of 154K users examined only 727 exhibited signs of pro-ISIS behaviour and the vast majority of those 727 users became \emph{activated} with such behaviour during the summer of 2014 when ISIS shared many beheading videos online; (ii) users exhibit significant behaviour divergence around the time of their activation, and; (iii) social homophily has a strong bearing on the diffusion process of pro-ISIS terms through Twitter.
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Carrera, Berny, and Jae-Yoon Jung. "SentiFlow: An Information Diffusion Process Discovery Based on Topic and Sentiment from Online Social Networks." Sustainability 10, no. 8 (August 2, 2018): 2731. http://dx.doi.org/10.3390/su10082731.
Повний текст джерелаАнотація:
In this digital era, people can become more interconnected as information spreads easily and quickly through online social media. The rapid growth of the social network services (SNS) increases the need for better methodologies for comprehending the semantics among the SNS users. This need motivated the proposal of a novel framework for understanding information diffusion process and the semantics of user comments, called SentiFlow. In this paper, we present a probabilistic approach to discover an information diffusion process based on an extended hidden Markov model (HMM) by analyzing the users and comments from posts on social media. A probabilistic dissemination of information among user communities is reflected after discovering topics and sentiments from the user comments. Specifically, the proposed method makes the groups of users based on their interaction on social networks using Louvain modularity from SNS logs. User comments are then analyzed to find different sentiments toward a subject such as news in social networks. Moreover, the proposed method is based on the latent Dirichlet allocation for topic discovery and the naïve Bayes classifier for sentiment analysis. Finally, an example using Facebook data demonstrates the practical value of SentiFlow in real world applications.
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Leyshon, Michael, and Matthew Rogers. "Designing for Inclusivity: Platforms of Protest and Participation." Urban Planning 5, no. 4 (October 14, 2020): 33–44. http://dx.doi.org/10.17645/up.v5i4.3258.
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This article offers critical insights into new digital forms of citizen-led journalism. Many communities across western society are frequently excluded from participating in newsgathering and information dissemination that is directly relevant to them due to financial, educational and geographic constraints. News production is a risky business that requires professional levels of skill and considerable finances to sustain. Hence, ‘hyper-localised news’ are often absent from local and national debates. Local news reportage is habitually relegated to social media, which represents a privileged space where the diffusion of disinformation presents a threat to democratic processes. Deploying a place-based, person-centred approach towards investigating news production within communities in Cornwall, UK, this article reflects on a participatory action research project called the Citizen Journalism News Network (CJNN). The CJNN is an overt attempt to design disruptive systems for agenda setting through mass participation and engagement with social issues. The project was delivered within four communities via a twelve-week-long journalism course, and a bespoke online app. CJNN is a platform for citizen journalists to work collaboratively on investigating stories and raising awareness of social issues that directly affect the communities reporting on them.
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Walia, Rhythm, and M. P. S. Bhatia. "Modeling Rumors in Twitter." International Journal of Rough Sets and Data Analysis 3, no. 4 (October 2016): 46–67. http://dx.doi.org/10.4018/ijrsda.2016100104.
Повний текст джерелаАнотація:
With the advent of web 2.0 and anonymous free Internet services available to almost everyone, social media has gained immense popularity in disseminating information. It has become an effective channel for advertising and viral marketing. People rely on social networks for news, communication and it has become an integral part of our daily lives. But due to the limited accountability of users, it is often misused for the spread of rumors. Such rumor diffusion hampers the credibility of social media and may spread social panic. Analyzing rumors in social media has gained immense attention from the researchers in the past decade. In this paper the authors provide a survey of work in rumor analysis, which will serve as a stepping-stone for new researchers. They organized the study of rumors into four categories and discussed state of the art papers in each with an in-depth analysis of results of different models used and a comparative analysis between approaches used by different authors.
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Schwemmer, Carsten. "The Limited Influence of Right-Wing Movements on Social Media User Engagement." Social Media + Society 7, no. 3 (July 2021): 205630512110416. http://dx.doi.org/10.1177/20563051211041650.
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This article generates new insights into the dynamic interplay between social media content generated by right-wing movements, user engagement, and the public attention movements receive. I argue that movement leaders seek to achieve high user engagement for utilizing mechanisms of information diffusion to increase both online and on-site mobilization. In a case study, I analyze the German right-wing movement Pegida, which uses Facebook for spreading its anti-Islam agenda online. Data from Pegida’s Facebook page are combined with news reports over a period of 18 months to measure activity on Facebook and in the public sphere simultaneously. Results of quantitative text and time series analysis show that Pegida cannot influence user engagement by simply creating more posts. Instead, it is the content of posts that matters. Moreover, findings highlight a strong connection between Facebook activities and the public sphere. In times of decreasing attention, the movement changes its social media strategy in response to exogenous shocks: Pegida resorts increasingly to radical mobilization methods by posting xenophobic content that is more likely to incite users to engage on Facebook.
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Arfini, Selene, Tommaso Bertolotti, and Lorenzo Magnani. "The Diffusion of Ignorance in On-Line Communities." International Journal of Technoethics 9, no. 1 (January 2018): 37–50. http://dx.doi.org/10.4018/ijt.2018010104.
Повний текст джерелаАнотація:
This article aims to investigate how information-sharing mechanisms in online communities favor activities of ignorance distribution on their platforms, such as fake data, biased beliefs, and inaccurate statements. In brief, the authors claim that online communities provide more ways to connect the users to one another rather than to control the quality of the data they share and receive. This, in turn, diminishes the value of fact-checking mechanisms in online news-consumption. The authors contend that while digital environments can stimulate the interest of groups of students and amateurs in scientific and political topics, the diffusion of false, poor, and un-validated data through digital media contributes to the formation of bubbles of shallow understanding in the digitally informed public. In brief, the present article is a philosophical research that applies the virtual niche construction theory to the cognitive behavior of internet users, as it is described by the current psychological, sociological, and anthropological literature.
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Andrews, Kenneth T., and Michael Biggs. "The Dynamics of Protest Diffusion: Movement Organizations, Social Networks, and News Media in the 1960 Sit-Ins." American Sociological Review 71, no. 5 (October 2006): 752–77. http://dx.doi.org/10.1177/000312240607100503.
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Sacco, Vincent F. "News that Counts: Newspaper Images of Crime and Victimization Statistics." Criminologie 33, no. 1 (October 2, 2002): 203–23. http://dx.doi.org/10.7202/004744ar.
Повний текст джерелаАнотація:
Abstract The study of media images of crime and victimization has tended to focus on the reporting of criminal events. However, the reporting of crime and victimization statistics is an important, if unaddressed theme in crime news coverage. Such statistics, as Joel Best and other social constructionists have argued, perform as important rhetorical devices in the social processes by which crime and (other social) problems are constructed and maintained. Such statistics are used to press claims about the pervasiveness and scope of new problems and therefore about the need for urgent social action. At the earliest stages, these claims may be issued by those who have no official status but who may be the only ones interested in the emergent issue. The legitimacy which statistics lend to social problems is relevant not only at the initial phases of construction, however. To remain on the public agenda, social problems require maintenance. And the regular diffusion of statistical information which purports to document shifts in the scope or size of the problem is essential to such maintenance. Most often, the role of collecting and disseminating statistical information regarding established problems is assumed by state agencies. In general, statistical claims about crime and other social problems reach the general public via the mass media - most importantly the news media. This paper examines news reporting about crime statistics which appeared in Canadian English language print media during the calendar years 1993 and 1994. A search of a computerized data base and a more detailed search of news items appearing during more intensive periods of statistical claim-making yielded a final sample of 244 news articles from major newspapers and newsmagazines. Two broad questions form the focus of the analysis. The first concerns the means by which statistical claims about crime and victimization enter the news flow. Put simply, to whose statistical claims about crime do journalists pay attention and what are the "news hooks" on which media discussions of rates, statistical trends and percentages are hung? The analysis finds that there are principally three routes by which crime statistics become news. The first and most common is the "data release" in the form of press conferences, the release of a new study or the regular release of data by state agencies. Not all of those who seek to make statistical claims of this type are equally likely to attract the attention of the media, and those agencies and individuals who occupy superordinate positions within a hierarchy of credibility are most likely to prove successful in this regard. Such credibility is most typically conveyed via the official status of the source. A second major form of news hook involves efforts at "debunking" or charges of statistical error. In these cases, "new" statistical findings call into question what earlier statistics encouraged audiences to believe. A third route by which crime and victimization statistics enter the news flow relates to the use of statistics as "background" information with respect to some more substantive theme. The second major question on which the paper is focused involves a consideration of the ways in which statistical news is packaged so as to ensure conformity with dominant news values. The analysis suggests that journalists employ a number of strategies to meet these objectives. Most important, there is considerable journalistic effort to dress stories involving statistics in ways that emphasize humour, drama and public interest. As well, an attempt is routinely made to emphasize the importance of reported statistics and the objective character of the reporting itself. A persistent criticism of media reporting of crime is that there is a clear journalistic preference for bad news. This analysis reveals, however, that journalists might be more interested in easy news than in bad news. The availability of official statistics (which in this sample, at least, often described stable or declining trends) and the reliance on liberal social scientists as a counterpoint to the more conservative voices of policing agencies and victims' organizations implies that the statistical images in the media are often more complex than they are assumed to be.
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Gaikwad, Tejaswi, Bhaskar Rajale, Prasad Bhosale, Swapnil Vedpathak, and Mrs S. S. Adagale. "Detection of fake news using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 1506–10. http://dx.doi.org/10.22214/ijraset.2022.48172.
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Abstract: Social media platforms like Facebook, Whatsapp, Twitter, and Telegram are important sources of information diffusion in the modern period, and people believe it with- out questioning its authenticity or source. Social media has fascinated people worldwide in spreading fake news due to its easy availability, cost-effectiveness, and ease of information sharing. The website – pmssgovt online claims that the scheme is applicable to all Indian students studying from class 9 to graduation degree. The website was fake and was charging Rs 450 as the registration fee for students to participate in the said scheme. Fake news can be generated to mislead the community for personal or commercial gains. It can also be used for other personal benefits such as defaming eminent personalities, amendment of government policies, etc. Thus, to mitigate the awful consequences of fake news, several research types have beenconducted for its detection with high accuracy to prevent its fatal outcome. This article proposes a framework for detecting fake news based on feature extraction and feature selection algorithmsand a set of voting classifiers. The proposed system distinguishes fake news from real news. First, we preprocessed the data taking unnecessary characters and numbers and reducing the words in the dictionary (lemmatization). Second, we extracted some important features by using two types of feature extraction, the term frequency-inverse document frequency technique and bag ofwords. Third, the extracted characteristics were reduced with the help of the different machine learning algorithm and the analysisof the variance algorithm. At last, challenges and open issues along with future research directions are discussed to facilitate the research in this domain further
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Arcila Calderón, Carlos, Eduar Barbosa Caro, and Ignacio Aguaded. "Modeling and diffusion of news topics in social media: Features and factors of the emergence of news in a Twitter informative channel." Comunicación y Sociedad 2019 (March 6, 2019): 1–21. http://dx.doi.org/10.32870/cys.v2019i0.6437.
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Leonardi, Simone, Giuseppe Rizzo, and Maurizio Morisio. "Automated Classification of Fake News Spreaders to Break the Misinformation Chain." Information 12, no. 6 (June 15, 2021): 248. http://dx.doi.org/10.3390/info12060248.
Повний текст джерелаАнотація:
In social media, users are spreading misinformation easily and without fact checking. In principle, they do not have a malicious intent, but their sharing leads to a socially dangerous diffusion mechanism. The motivations behind this behavior have been linked to a wide variety of social and personal outcomes, but these users are not easily identified. The existing solutions show how the analysis of linguistic signals in social media posts combined with the exploration of network topologies are effective in this field. These applications have some limitations such as focusing solely on the fake news shared and not understanding the typology of the user spreading them. In this paper, we propose a computational approach to extract features from the social media posts of these users to recognize who is a fake news spreader for a given topic. Thanks to the CoAID dataset, we start the analysis with 300 K users engaged on an online micro-blogging platform; then, we enriched the dataset by extending it to a collection of more than 1 M share actions and their associated posts on the platform. The proposed approach processes a batch of Twitter posts authored by users of the CoAID dataset and turns them into a high-dimensional matrix of features, which are then exploited by a deep neural network architecture based on transformers to perform user classification. We prove the effectiveness of our work by comparing the precision, recall, and f1 score of our model with different configurations and with a baseline classifier. We obtained an f1 score of 0.8076, obtaining an improvement from the state-of-the-art by 4%.