Journal articles: 'X (formely Twitter)'

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Koloski, Natasha, Kerith Duncanson, Shanthi Ann Ramanathan, Melanie Rao, Gerald Holtmann, and Nicholas J. Talley. "What impact has the Centre of Research Excellence in Digestive Health made in the field of gastrointestinal health in Australia and internationally? Study protocol for impact evaluation using the FAIT framework." BMJ Open 14, no. 3 (March 2024): e076839. http://dx.doi.org/10.1136/bmjopen-2023-076839.

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IntroductionThe need for public research funding to be more accountable and demonstrate impact beyond typical academic outputs is increasing. This is particularly challenging and the science behind this form of research is in its infancy when applied to collaborative research funding such as that provided by the Australian National Health and Medical Research Council to the Centre for Research Excellence in Digestive Health (CRE-DH).Methods and analysisIn this paper, we describe the protocol for applying the Framework to Assess the Impact from Translational health research to the CRE-DH. The study design involves a five-stage sequential mixed-method approach. In phase I, we developed an impact programme logic model to map the pathway to impact and establish key domains of benefit such as knowledge advancement, capacity building, clinical implementation, policy and legislation, community and economic impacts. In phase 2, we have identified and selected appropriate, measurable and timely impact indicators for each of these domains and established a data plan to capture the necessary data. Phase 3 will develop a model for cost–consequence analysis and identification of relevant data for microcosting and valuation of consequences. In phase 4, we will determine selected case studies to include in the narrative whereas phase 5 involves collation, data analysis and completion of the reporting of impact.We expect this impact evaluation to comprehensively describe the contribution of the CRE-DH for intentional activity over the CRE-DH lifespan and beyond to improve outcomes for people suffering with chronic and debilitating digestive disorders.Ethics and disseminationThis impact evaluation study has been registered with the Hunter New England Human Research Ethics Committee as project 2024/PID00336 and ethics application 2024/ETH00290. Results of this study will be disseminated via medical conferences, peer-reviewed publications, policy submissions, direct communication with relevant stakeholders, media and social media channels such as X (formely Twitter).

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Olanrewaju, Ayishat Sandra. "Audience’s Perception of X (Formerly Twitter) Trends as a Tool for the Nigerian Public Sphere." American Journal of Communication 6, no. 2 (July 16, 2024): 42–64. http://dx.doi.org/10.47672/ajc.2216.

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Purpose: Unlike how we previously knew it to be, the public sphere has evolved to include digital spaces, making them metamorphose into digital public spheres. These digital spaces are more open, accessible, and void of certain bottlenecks. It is, therefore, now impossible to describe a public sphere without mentioning digital spaces. As such, this study aims to find out the audience's perception of X (formerly Twitter) trends as a tool for the Nigerian public sphere. Materials and Methods: The survey research method was used to obtain responses from 200 respondents, using the purposive sampling technique of selecting only X (formerly Twitter) users. Findings: The findings reveal that most X (formerly Twitter) users often utilise X (formerly Twitter) trends to some extent; X (formerly Twitter) trends influence the content of some users but not the majority of them, even if the majority use the feature. In addition, X (formerly Twitter) Trends highlight significant content for X (formerly Twitter) users in Nigeria. Implications to Theory, Practice and Policy: Some of the recommendations of the study include the following: the government should pay closer attention to relevant public conversations on the X (formerly Twitter) platform to understand people’s take on certain issues and how to help them better; they should also partner with relevant agencies to set up regular digital literacy campaigns to help the public effectively engage with social media content on X (formerly Twitter). Also, stakeholders like X (formerly Twitter) should ensure suitable systems are in place for accurate Trends metrics to avoid misleading users with the wrong Trends or content by some users. In addition, X (formerly Twitter) should develop campaigns and guides to help users better understand how to engage with the Trends feature on X (formerly Twitter).

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Kimani, Joshua, Anthony Karanjah, and Pius Kihara. "Sentiment Classification of Safaricom PLC Social Media Sentiments on X(Formerly Twitter)." Asian Journal of Probability and Statistics 26, no. 6 (May 31, 2024): 31–40. http://dx.doi.org/10.9734/ajpas/2024/v26i6622.

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In today's digital era, social media plays a pivotal role in shaping public sentiment, particularly in the financial domain. This study focuses on sentiment analysis of social media discussions, specifically tweets discussing Safaricom PLC on X (formerly Twitter), leveraging Natural Language Processing (NLP) techniques. By meticulously collecting, cleaning, and analyzing data, valuable insights into the sentiment landscape surrounding Safaricom PLC during a significant period were obtained. The sentiment analysis, conducted using the VADER lexicon, categorized sentiments into positive, negative, and neutral classes. Notably, the analysis revealed a predominant positive sentiment, indicative of an optimistic tone in discussions related to Safaricom PLC. This study highlights the potential of integrating sentiments and sentiment analysis techniques into stock price prediction models to facilitate informed investment decision-making.

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Makaruke, Kumbirai, and Akim Munthali. "Analysis of X (Formerly Twitter) Users’ Perceptions towards the Election of Jolidee Matongo as Mayor of Johannesburg." International Journal of Research and Innovation in Social Science VIII, no. IV (2024): 502–9. http://dx.doi.org/10.47772/ijriss.2024.804039.

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This paper presents a study on the analysis of X (formerly Twitter) users’ perceptions towards the election of Jolidee Matongo as mayor of Johannesburg, South Africa in 2021. This election was significant because it was the first time that a person of Zimbabwean descent had been elected mayor of Johannesburg. Given the vast amount of user-generated content on Twitter, analysing tweets can serve as a valuable proxy for capturing public opinion. However, to date, there appears to be a notable research gap concerning the wide adoption of tweet analysis for understanding public opinion. The study used 4324 tweets that were collected from X (Twitter) via the Twitter application programming interface (API) over a period of one week after the election. Sentiment analysis and topic modelling were used to analyse the results. The results suggested that social media platforms can be used to gain valuable insights into public opinion and the different perspectives. Social media platforms can also be used to spread xenophobia. Cognitive biases, specifically the confirmation bias has been found to influence Twitter users’ perception towards the election of Matongo. A significant number of negative tweets expressed xenophobic views due to Matongo’s Zimbabwean roots. However, the average sentiment score of 0.10 suggested that the overall sentiment of the tweets was slightly positive.

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Elmas, Tuğrulcan, Mathis Randl, and Youssef Attia. "#TeamFollowBack: Detection & Analysis of Follow Back Accounts on Social Media." Proceedings of the International AAAI Conference on Web and Social Media 18 (May 28, 2024): 381–93. http://dx.doi.org/10.1609/icwsm.v18i1.31321.

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Follow back accounts inflate their follower counts by engaging in reciprocal followings. Such accounts manipulate the public and the algorithms by appearing more popular than they really are. Despite their potential harm, no studies have analyzed such accounts at scale. In this study, we present the first large-scale analysis of follow back accounts. We formally define follow back accounts and employ a honeypot approach to collect a dataset of such accounts on X (formerly Twitter). We discover and describe 12 communities of follow back accounts from 12 different countries, some of which exhibit clear political agenda. We analyze the characteristics of follow back accounts and report that they are newer, more engaging, and have more followings and followers. Finally, we propose a classifier for such accounts and report that models employing profile metadata and the ego network have some success, although achieving high recall is challenging. Our study enhances understanding of the follow back accounts and discovering such accounts in the wild.

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Arnett, Autumn A. "Is AI making faculty's jobs worse?" Enrollment Management Report 28, no. 8 (October 22, 2024): 5–7. http://dx.doi.org/10.1002/emt.31310.

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Rua Mae Williams, an assistant professor in the User Experience Design program at Purdue University, recently shared on X (the network formerly known as Twitter) the ways implicit bias about who and what intelligence looks and sounds like inspires greater use of artificial intelligence platforms, such as ChatGPT, among students who don’t feel they measure up.

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Arnett, Autumn A. "Is AI making faculty's jobs worse?" Successful Registrar 24, no. 9 (October 22, 2024): 5–7. http://dx.doi.org/10.1002/tsr.31367.

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Rua Mae Williams, an assistant professor in the User Experience Design program at Purdue University, recently shared on X (the network formerly known as Twitter) the ways implicit bias about who and what intelligence looks and sounds like inspires greater use of artificial intelligence platforms, such as ChatGPT, among students who don’t feel they measure up.

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Samalis, Alexandros, Alexandros Z. Spyropoulos, Georgios C. Makris, Charalampos Bratsas, Andreas Veglis, Vassilis Tsiantos, Anthoula Baliou, Emmanouel Garoufallou, and Anastasios Ventouris. "Data Journalism and Network Theory: A Study of Political Communication through X (Formerly Twitter) Interactions." Journalism and Media 4, no. 4 (November 25, 2023): 1141–68. http://dx.doi.org/10.3390/journalmedia4040073.

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This study investigates the research questions: “How do political connections within Greece’s governing party evolve, and what underlying patterns and dynamics are revealed through a network analysis of interactions on X (formerly Twitter)?” To address these questions, data were collected from X, focusing on following, retweeting, and mentioning activities among the politicians within the governing party. The interactions were meticulously analysed using tools derived from Network Theory in mathematics, including in and out-strength centrality, hubs and authorities centralities, and in and out-vertex entropy. In line with the emerging field of data journalism, this approach enhances the rigour and depth of analysis, facilitating a more nuanced understanding of complex political landscapes. The findings reveal complex and dynamic structures that may reflect internal relationships, communication strategies, and the influence of recurring events on these connections within the party. This study thus provides novel insights into understanding political communication via social networks and demonstrates the applicative potential of Network Theory and data journalism techniques in social sciences.

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Jahanbin, Kia, Mohammad Jokar, and Vahid Rahmanian. "Using X Social Networks (formerly Twitter) and web news mining to predict the measles outbreak." Asian Pacific Journal of Tropical Medicine 17, no. 4 (April 2024): 188–90. http://dx.doi.org/10.4103/apjtm.apjtm_194_24.

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Yum, Seungil. "Capturing human response to Winter Storm Frankie based on X (formerly known as Twitter) data." Journal of Emergency Management 22, no. 6 (December 1, 2024): 611–19. https://doi.org/10.5055/jem.0827.

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This study delves into how people responded to Winter Storm Frankie in the United States based on X (formerly known as Twitter®) data according to a multitude of regions, periods, sociodemographic characteristics, census regions, and geographical scales. This study finds that people actively respond to natural disasters on X during the winter storm week. Specifically, the highest number of keywords during the winter storm week is 1.6 times greater than the second-highest number of keywords during the prewinter storm week. Second, the spatial distribution of tweets exhibits significant fluctuations across different periods. For instance, in the prewinter storm week, more tweets are posted in the West region, while in the winter storm week, the Northeast region experiences a higher volume of uploads. Third, regional variables exert a substantial influence on the number of tweets. For instance, Ohio and Montana demonstrate higher elasticity than Pennsylvania. Fourth, many sociodemographic variables, such as gender, age, education, and income, are associated with individual responses. For example, a 1 percent increase in males corresponds to a 0.01 percent increase in tweets.

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Li, Oscar, Tia Cheunkarndee, Hilit F. Mechaber, and Katherine Chretien. "Using Instagram to Engage Learners and Build Community." Academic Medicine 99, no. 5 (May 2024): 586. http://dx.doi.org/10.1097/acm.0000000000005558.

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The COVID-19 pandemic exacerbated the shift toward using digital means of communication, information sharing, and building community.1 This phenomenon has manifested in the world of medical education with an increase in educators and learners using social media. While the impacts of X (formerly Twitter) and Facebook have been investigated, there is a paucity of literature on Instagram usage in medical education.

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Demirci, Mehmet Ali, Pembe Oltulu, and Sanjay Mukhopadhyay. "The educational impact of the hashtag PathTweetAward on pathology from the perspective of medical students." Journal of Diagnostic and Academic Pathology 1, no. 1 (January 2024): 13–17. http://dx.doi.org/10.4103/jdap.jdap_1_24.

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ABSTRACT Introduction: Pathology is a field that extensively utilizes X, formerly Twitter, in education, communication, and collaboration. It is widely utilized by numerous professionals and trainees, such as medical students and residents, and uses many valuable hashtags. Using the hashtag PathTweetAward on platform X (formerly Twitter), outstanding tweets and posts can be tagged for consideration for an award known as #PathTweetAward. Aim: This study examined the impact of the #PathTweetAward on medical students, pathologists, pathology residents, and physicians in other specialties’ educational experience and the awareness of the award among educational social media users. Materials and Methods: The population surveyed comprised 80 of 84 full-time medical students and 4 of 84 physicians from different medical faculties in Turkiye. Since not enough physicians could be reached in the population, the data of 4 physicians were excluded from the study. Conclusions: Online and in-person surveys with Turkish medical students from various faculties found PathTwitter on X a great educational tool for students, residents, and pathologists. Furthermore, it is a valuable forum for expert discussions. Pathology uses X most in education, communication, and collaboration. Medical students and residents use its many hashtags. Despite its low prevalence, most #PathTweetAward users found it educational. Our findings are crucial to understanding social media’s educational potential. Our research shows that social media can help people learn and share, but #PathTweetAward needs more promotion. Social media and pathology education should be studied with larger groups and innovative tools like #PathTweetAward promoted.

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Loureiro-Porto, Lucía, and José Luis Ariza-Fernández. "Nonbinary pronouns in X (Twitter) bios: Gender and identity in online spaces." Research in Corpus Linguistics 13, no. 1 (2024): 171–96. http://dx.doi.org/10.32714/ricl.13.01.08.

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This study explores the usage of nonbinary pronouns on X (formerly known as Twitter), focusing on THEY and neopronouns like ZE or XE within the nonbinary community. Building on the increasing practice of sharing pronouns, especially in online spaces, the research collects 1,980 X accounts using Followerwonk. Despite ideological differences across U.S. regions, no substantial variations in pronoun usage are observed. Notably, a preference for rolling pronouns (e.g., they/she) emerges, with fewer instances of monopronoun usage (e.g., they). When a single pronoun is chosen, it is often accompanied by the respective accusative form, while rolling pronoun users tend to omit the accusative. Users with binary pronouns often prioritize it as their first chosen pronoun. THEY remains the predominant nonbinary pronoun, with neopronouns being rare. The study highlights X profiles as valuable sources for understanding linguistic patterns related to social trends, particularly in the context of gender equality and network relations.

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Hassani, Hossein, Nadejda Komendantova, Elena Rovenskaya, and Mohammad Reza Yeganegi. "Social Intelligence Mining: Unlocking Insights from X." Machine Learning and Knowledge Extraction 5, no. 4 (December 11, 2023): 1921–36. http://dx.doi.org/10.3390/make5040093.

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Social trend mining, situated at the confluence of data science and social research, provides a novel lens through which to examine societal dynamics and emerging trends. This paper explores the intricate landscape of social trend mining, with a specific emphasis on discerning leading and lagging trends. Within this context, our study employs social trend mining techniques to scrutinize X (formerly Twitter) data pertaining to risk management, earthquakes, and disasters. A comprehensive comprehension of how individuals perceive the significance of these pivotal facets within disaster risk management is essential for shaping policies that garner public acceptance. This paper sheds light on the intricacies of public sentiment and provides valuable insights for policymakers and researchers alike.

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Yousafzai, Abdul Wadood, and Shabir Hussian. "Digital Propaganda in South Asia: An Analytical Perspective." Global International Relations Review VI, no. II (September 30, 2024): 92–100. http://dx.doi.org/10.31703/girr.2023(vi-ii).10.

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This article examines the existing response strategies of Pakistan to Indian digital propaganda on the Kashmir Issue. By analyzing key propaganda strategies on X (formerly Twitter), state-sponsored X accounts, media reports, and academic scholarship, the researcher has identified a number of deficiencies in the prevalent approach and offers policy recommendations for better practices. Overall, this article recommends that Pakistan should articulate a response strategy that has clear objectives, is powered by algorithms and computational techniques, should include news actors, and utilize the void created by India's state-backed digital propaganda machinery.

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Guswi, Arsita Julianda, Annissa Ramadhani, Aryo Mulyo Satrio Negoro, and Teguh Sarosa. "Linguistic Patterns Among Pop Culture Discourse on @westenthu X or Twitter Platform." JELITA 6, no. 1 (December 29, 2024): 61–77. https://doi.org/10.56185/jelita.v6i1.872.

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This study examines how pop culture enthusiasts use language on the @westenthu auto-base account to express identity and foster community in digital spaces. Tweets from October 3 to October 5, 2024, are analysed qualitatively through document analysis to identify linguistic patterns, contextual usage, and cultural dynamics. The limited timeframe was chosen to ensure a focused, detailed examination of interactions without overwhelming data volume, which is essential for qualitative research. Findings reveal the frequent use of code-switching, code-mixing (e.g., “drop ur fav Christmas wst movies yang seru plss”), slang (e.g., “nonton Princess Diaries dimana ya? Tia!”), and informal language, illustrating the dynamic interplay between language and identity in online communication. These linguistic practices show how platforms like X (formerly Twitter) not only facilitate interaction but also create spaces for identity construction and rapid language evolution. By analysing specific language features, this research offers insights into the intersection of sociolinguistics and digital communication, highlighting how language adapts and thrives in digital environments.

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Pramesti, Elsa Vina, and Indah Wenerda. "Utilization of @NCTDreamINA X autobase account as interaction media among NCT Dream Fans in Indonesia." Symposium of Literature, Culture, and Communication (SYLECTION) 2022 3, no. 1 (November 22, 2023): 1022. http://dx.doi.org/10.12928/sylection.v3i1.14147.

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The utilization of autobase accounts on X (formerly known as Twitter) is quite phenomenal for X users in Indonesia, including fans of the South Korean boy group NCT Dream in Indonesia as a forum for interaction and exchange of information for fans through existing autobase accounts. This study aims to determine how the utilization of the autobase account @NCTDreamINA on X as interaction media among NCT Dream fans in Indonesia. This research uses descriptive qualitative research methods with a netnography approach. The results showed that the @NCTDreamINA autobase account on X was well utilized by its followers (NCT Dream fans). In this case, the @NCTDreamINA account is able to realize the information needs of its followers.

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Rao, Varun K., Danny Valdez, Rasika Muralidharan, Jon Agley, Kate S. Eddens, Aravind Dendukuri, Vandana Panth, and Maria A. Parker. "Digital Epidemiology of Prescription Drug References on X (Formerly Twitter): Neural Network Topic Modeling and Sentiment Analysis." Journal of Medical Internet Research 26 (August 23, 2024): e57885. http://dx.doi.org/10.2196/57885.

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Background Data from the social media platform X (formerly Twitter) can provide insights into the types of language that are used when discussing drug use. In past research using latent Dirichlet allocation (LDA), we found that tweets containing “street names” of prescription drugs were difficult to classify due to the similarity to other colloquialisms and lack of clarity over how the terms were used. Conversely, “brand name” references were more amenable to machine-driven categorization. Objective This study sought to use next-generation techniques (beyond LDA) from natural language processing to reprocess X data and automatically cluster groups of tweets into topics to differentiate between street- and brand-name data sets. We also aimed to analyze the differences in emotional valence between the 2 data sets to study the relationship between engagement on social media and sentiment. Methods We used the Twitter application programming interface to collect tweets that contained the street and brand name of a prescription drug within the tweet. Using BERTopic in combination with Uniform Manifold Approximation and Projection and k-means, we generated topics for the street-name corpus (n=170,618) and brand-name corpus (n=245,145). Valence Aware Dictionary and Sentiment Reasoner (VADER) scores were used to classify whether tweets within the topics had positive, negative, or neutral sentiments. Two different logistic regression classifiers were used to predict the sentiment label within each corpus. The first model used a tweet’s engagement metrics and topic ID to predict the label, while the second model used those features in addition to the top 5000 tweets with the largest term-frequency–inverse document frequency score. Results Using BERTopic, we identified 40 topics for the street-name data set and 5 topics for the brand-name data set, which we generalized into 8 and 5 topics of discussion, respectively. Four of the general themes of discussion in the brand-name corpus referenced drug use, while 2 themes of discussion in the street-name corpus referenced drug use. From the VADER scores, we found that both corpora were inclined toward positive sentiment. Adding the vectorized tweet text increased the accuracy of our models by around 40% compared with the models that did not incorporate the tweet text in both corpora. Conclusions BERTopic was able to classify tweets well. As with LDA, the discussion using brand names was more similar between tweets than the discussion using street names. VADER scores could only be logically applied to the brand-name corpus because of the high prevalence of non–drug-related topics in the street-name data. Brand-name tweets either discussed drugs positively or negatively, with few posts having a neutral emotionality. From our machine learning models, engagement alone was not enough to predict the sentiment label; the added context from the tweets was needed to understand the emotionality of a tweet.

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Shahzad, Dr Farrukh, Ms Tabinda Sadiq, and Dr Muhammad Umer Hayat. "Analyzing Social Media And Traditional Media Coverage Of Elections 2018 In Pakistan." Migration Letters 21, S5 (February 13, 2024): 1120–28. http://dx.doi.org/10.59670/ml.v21is5.8056.

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The study has been designed to investigate the social media and traditional media coverage of elections 2018 in Pakistan. For social media, X (formerly Known as Twitter)) was selected and for traditional media, Geo News and Dawn Newspaper were selected. The media coverage of the selected media was examined from 2nd July 2018 to 25th July 2018 in four equal time spans. The study uses agenda setting as a theoretical frame. Using content analysis, the study addresses research questions regarding first-level agenda-setting. The results indicated that the issue agendas of X, Geo News, and Dawn were similar during the elections 2018 in Pakistan.

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Kumar, Yulia, Kuan Huang, Angelo Perez, Guohao Yang, J. Jenny Li, Patricia Morreale, Dov Kruger, and Raymond Jiang. "Bias and Cyberbullying Detection and Data Generation Using Transformer Artificial Intelligence Models and Top Large Language Models." Electronics 13, no. 17 (August 29, 2024): 3431. http://dx.doi.org/10.3390/electronics13173431.

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Despite significant advancements in Artificial Intelligence (AI) and Large Language Models (LLMs), detecting and mitigating bias remains a critical challenge, particularly on social media platforms like X (formerly Twitter), to address the prevalent cyberbullying on these platforms. This research investigates the effectiveness of leading LLMs in generating synthetic biased and cyberbullying data and evaluates the proficiency of transformer AI models in detecting bias and cyberbullying within both authentic and synthetic contexts. The study involves semantic analysis and feature engineering on a dataset of over 48,000 sentences related to cyberbullying collected from Twitter (before it became X). Utilizing state-of-the-art LLMs and AI tools such as ChatGPT-4, Pi AI, Claude 3 Opus, and Gemini-1.5, synthetic biased, cyberbullying, and neutral data were generated to deepen the understanding of bias in human-generated data. AI models including DeBERTa, Longformer, BigBird, HateBERT, MobileBERT, DistilBERT, BERT, RoBERTa, ELECTRA, and XLNet were initially trained to classify Twitter cyberbullying data and subsequently fine-tuned, optimized, and experimentally quantized. This study focuses on intersectional cyberbullying and multilabel classification to detect both bias and cyberbullying. Additionally, it proposes two prototype applications: one that detects cyberbullying using an intersectional approach and the innovative CyberBulliedBiasedBot that combines the generation and detection of biased and cyberbullying content.

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Ismail, Nor Eisya Shabila, and Su-Hie Ting. "Have Your Say! Malaysian X (Twitter) Users Speak Their Minds About COVID-19 Vaccination." HUMAN BEHAVIOR, DEVELOPMENT and SOCIETY 25, no. 1 (April 18, 2024): 51–62. http://dx.doi.org/10.62370/hbds.v25i1.269145.

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In this study, Malaysian X (formerly Twitter) users’ views were examined on COVID-19 vaccination. The specific objectives were to identify issues that were important to X users, and identify changes in views on COVID-19 vaccination. Tweets were collected from 1 January to 31 December 2021; altogether 5,766 tweets (199,900 words) were collected, and 150 tweets (5,200 words) were systematically selected for analysis. Thematic analysis showed that the tweets were more concerned about administration of the COVID-19 vaccine (56.7%) than its impact (35.3%) or COVID-19 control measures (8%). Positive sentiments increased during the 12 months. In Phase 1 (1 January–23 February 2021), the public were uncertain and sceptical while waiting for vaccination. In Phase 2 (24 February–24 September 2021) when vaccination was underway, the tweets reflected an informed stance, and X users were even proactive in promoting vaccination benefits and correcting misinformation. By Phase 3 (25 September–31 December 2021) when vaccination for teenagers and s booster shot program began, there was a dilemma of wanting to return to normal life vis-à-vis prioritizing health and safety. The study data indicated more anti- than pro-vaccination tweets, but the X community had self-correcting mechanisms when vaccine hesitancy surfaced.

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Muciño, Mariana González, Dr Veronika De La Cruz Villegas, and Prof Silvia Patricia Aquino Zuñiga. "Linguistic Benefits of Social Interaction in the K-Pop X (Formerly Known As Twitter) Communities for Translation Trainees." International Journal of Humanities and Social Science Invention 13, no. 11 (November 2024): 11–17. http://dx.doi.org/10.35629/7722-13111117.

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Translators have a great opportunity to acquire a language in an immersive environment through social networks, with native and non-native speakers, which helps to strengthen their language skills and therefore increase their confidence in understanding and using it with others, this results in an increase in student motivation to continue learning (Ghafar 2023, Putra Nasution 2022). The purpose of this study was to identify the linguistic benefits that social interaction in K-Pop communities on X brings for translation trainees. We adopted a mixed exploratory and descriptive research approach, and one-on-one interviews were conducted on five students from the BA in Modern Languages (in the field of translation) from a Mexican university. Findings of this study show that translation trainees can take advantage of social networking as a way of immersion for language acquisition. Hence students can take advantage from their personal preferences and learn things that they can use in their professional careers. We hope that this research opens a new window of opportunity to what could be a new form of language immersion.

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Rizqi Nandadita Pamungkas, Didi Permadi, and Ike Desi Florina. "Strategi Humor Gibran Rakabuming dalam Komunikasi Politik di Media Sosial X (Twitter)." Jurnal Pemerintahan dan Politik 9, no. 3 (June 24, 2024): 175–82. http://dx.doi.org/10.36982/jpg.v9i3.4057.

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This research aims to analyze how the Vice President of Indonesia, Gibran Rakabuming, uses humor in political communication via the social media platform "X" (formerly known as Twitter). In a context where social media has become a primary channel for political communication, the use of humor has proven to be an effective tool for attracting public attention and shaping perceptions of political leaders. Qualitative content analysis methods were used to identify the types of humor used by Gibran, as well as its purpose and impact in a political context. Data was collected through direct observation of Gibran's uploads which were related to political communication and contained elements of humor. The research results show that Gibran uses humor for various purposes. Apart from building a positive image as a leader who is close to the people, Gibran also uses humor to convey political messages in a way that is more relaxed and easier for the public to understand. The positive response from the public to Gibran's use of humor in political communication shows its effectiveness in strengthening relations between leaders and society, as well as increasing public involvement in the political issues discussed. This research provides an in-depth understanding of how the use of humor influences public perceptions of political leaders in the digital era. Practical implications include the importance of understanding the strategic role of social media in building a positive public image and strengthening connections between leaders and their constituents through the use of humor in political communication.

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Somuncu, Burak. "Legitimizing Mediated Islamophobic Actions: Example “X”." Medya ve Din Araştırmaları Dergisi 7, no. 1 (June 13, 2024): 29–46. http://dx.doi.org/10.47951/mediad.1389899.

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The media plays an active role in spreading Islamophobia, with an increase in Islamophobic content and discourses in new media in recent years leading academic studies to focus on this issue. New media tools offer free and unlimited sharing opportunities to their users, becoming platforms where hate speech against religious groups is developed and spread. One such platform where freedom of speech is violated is X, formerly known as Twitter. It is crucial to determine the approach of content on social media platforms towards Islam, Muslims, and their sacred values. This study discusses posts made through burning/tearing actions on the X platform, covering the first 9 months of 2023. The findings, based on posts from 23 X accounts analyzed using qualitative-oriented content analysis, reveal that concepts related to Islam and Muslims with negative connotations were frequently used. The posts framed a narrative portraying Islam and Muslims as pro-violence, intolerant, and hostile. Acts of burning and tearing the Holy Quran were largely justified as freedom of speech, with a narrative of exclusion, accusation, distortion, and disdain towards Muslims who disagreed.

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Artika, Zahwa Dewi, and Erwin Budi Setiawan. "CONTENT-BASED FILTERING CULINARY RECOMMENDATION SYSTEM USING DEEP CONVOLUTIONAL NEURAL NETWORK ON TWITTER (X)." JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) 10, no. 2 (November 19, 2024): 333–41. http://dx.doi.org/10.33480/jitk.v10i2.5640.

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Along with the development of technology, social media has become integral to everyday life, especially for sharing content like culinary reviews. Social media platform X (formerly Twitter) is often used for sharing culinary recommendations, but the abundance of information makes it difficult for users to find relevant suggestions. In order to improve rating prediction performance, this study suggests a recommendation system model that is more thoroughly created utilizing Content-Based Filtering (CBF) combined with Deep Convolutional Neural Network (CNN) and optimised with Particle Swarm Optimization (PSO). Data was collected from PergiKuliner and Twitter, totaling 2644 reviews and 200 cuisines. The preprocessing involved text processing, translation, and polarity assessment. Post-labeling, 7438 data were labeled with 0 and 1562 with 1. Label 0 means not recommended while label 1 means recommended. The imbalance is handled by applying the SMOTE method after observing that the fraction of data labeled 0 and 1 is 65.2%. CBF employed TF-IDF feature extraction and FastText word embedding, while Deep CNN handled classification. PSO optimisation was applied to enhance the accuracy of culinary rating predictions. The results showed an initial accuracy of 76.32% with the baseline Deep CNN model, which increased to 86.06% after Nadam optimisation with the best learning rate, and further reached 86.18% after PSO optimisation on dense units. The 9.86% accuracy improvement from the baseline model demonstrates the effectiveness of the combined methods.

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Juciananda Febriamita, Eliza Abelia, Nayla Zahratul Maula, and Ita Ita. "Pemerolehan Leksikon Ragam Bahasa Gaul pada Aplikasi X." Jurnal Pendidikan, Bahasa dan Budaya 3, no. 4 (December 2, 2024): 62–69. https://doi.org/10.55606/jpbb.v3i4.4560.

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The study used a qualitative descriptive approach method. The purpose of this study was to determine the acquisition of slang lexicon by teenagers on the X application, providing insight into language in the modern era, the role of social media in language development, understanding slang in forming individual and community identities in social media, and understanding rapid language changes due to technological developments. Language variety is a variation of language that differs according to the context of use, including the relationship between the speaker and the listener. Slang, as a form of variation that continues to develop, is heavily influenced by social media, especially the X application (formerly Twitter), which is the main platform for teenagers. Through interactions on social media, users learn and absorb new vocabulary quickly, which shapes their social identity. This study used a qualitative approach to analyze the slang lexicon that appears in the application, finding categories such as abbreviations, foreign words, and phonological changes. Although slang enriches communication, there is a risk of reducing the use of formal language that needs to be balanced with language literacy education.

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Wadood, Abdul, and Shabir Hussian. "Analyzing Digital Propaganda on X through Topic Modelling Approach: The Case of Afghanistan." Global Strategic & Securities Studies Review VIII, no. II (June 30, 2024): 102–9. http://dx.doi.org/10.31703/gsssr.2023(viii-ii).11.

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The dissemination of propaganda has become a formidable weapon, with nation-states exploiting social media platforms to engineer narratives favorable to their geopolitical interests. This study delved into Indo-Pakistan states' orchestrated propaganda in the wake of the Taliban's recapture of Afghanistan. This period is marked by a sophisticated blend of propaganda strategies to mold online discourse. Utilizing a dataset derived from X (formerly Twitter), the research examines how Pakistan and India leveraged information warfare to advance their politico-diplomatic narratives, shedding light on the propaganda strategies of such digital warfare and the emergence of narratives and counter-narratives. The findings highlight the instrumental role of propaganda strategies in amplifying political, diplomatic, security, and humanitarian narratives and manipulating public discourse, with distinct tactics and officially approved state maneuvers identified through the topic modeling and analyzing key strategies of propaganda.

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Khairunnas, Rezki, Jeri Apriansyah Pagua, Ghina Fitriya, and Yova Ruldeviyani. "User sentiment dynamics in social media: a comparative analysis of X and Threads." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 1 (February 1, 2025): 447. http://dx.doi.org/10.11591/ijai.v14.i1.pp447-456.

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This research examines the dynamics of user sentiment and its correlation with the usage factors of applications in the context of the competition between X (formerly Twitter) and Threads, a social media application under the umbrella of Meta. Through sentiment analysis of user reviews on the Google Play Store and App Store, the study aims to identify the key factors contributing to a significant decline in user engagement with Threads and the return of users to X. The method employed in this research is the support vector machine (SVM) for sentiment classification of reviews. The study then correlates the classified sentiments with application usage factors: usability, features, design, and support. The research findings indicate user sentiment influences user engagement, especially in features and design. The research concludes with insights regarding implications for application developers and suggests directions for future research.

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Meleshchenko, Olga. "Multimodal metaphtonymy in internet memes: A response to Donald Trump’s mug shot on X (formerly twitter) as a case study." Cognition, Communication, Discourse, no. 28 (August 25, 2024): 78–90. http://dx.doi.org/10.26565/2218-2926-2024-28-05.

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This study examines multimodal metaphtonymy in memes responding to Trump’s 2023 mugshot post on his X (formerly Twitter) account, @realDonaldTrump. The author employs the methodological tools of the conceptual (multimodal) metaphor and metonymy theory to identify patterns of interaction between metaphor and metonymy in these memes. The results reveal three types of multimodal metaphtonymy: metaphtonymy with a metonymy incorporated into either the metaphoric source or target, metaphtonymy with a metonymy incorporated into both the metaphoric source and target, and metaphtonymy with a metonymic chain structuring the metaphoric source. The metaphoric target domain of these metaphtonymies is identified as TRUMP-US PRESIDENT, reflecting Trump’s status as the 45th US President, a political leader, and a candidate for the 2024 US presidency within X platform. The study provides an in-depth analysis of each identified type of multimodal metaphtonymy instantiated by metaphorical portrayals of Trump as A CRIMINAL PSYCHOPATH, A TODDLER HAVING A TANTRUM, and A LIAR. The source domains of these metaphors map exclusively negative characteristics onto the TRUMP-US PRESIDENT target domain, with some features shared across several metaphoric source domains. The recurrent negative portrayals in memes create powerful and enduring images that shape public perception, contributing to a lasting tarnished image of Trump. These portrayals highlight the incongruity between Trump’s constructed image and the expectations of a US political leader, leading viewers to question his fitness for the presidency. The continuous negative portrayal undermines Trump’s credibility and reinforces the perception of his unfitness for leadership.

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Suwarno, Suwarno, Mangapul Siahaan, and Annisya Putri Nadhia. "Analysis of Rebranding the X Application on User Loyalty in Batam City." WACANA: Jurnal Ilmiah Ilmu Komunikasi 22, no. 2 (December 30, 2023): 369–79. http://dx.doi.org/10.32509/wacana.v22i2.3408.

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The global market is currently experiencing very fierce competition. Companies compete to implement all marketing strategies to be superior in surviving this competition, one of which is a rebranding strategy by changing the brand image of the X application which was formerly known as Twitter. This study aims to determine whether the effect of rebranding can affect the loyalty of X’s users by assessing brand trust, brand prestige, and brand love. This research method uses a mixed method which is divided into two approaches, namely quantitative and qualitative using linear regression analysis. The results show brand image does not have a big influence on brand trust, brand prestige, and brand love. Furthermore, brand trust, brand prestige, and brand love have a positive influence on brand loyalty meaning that users are not too affected by the rebranding of X, but they will remain loyal to using the application.

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Gupta, Anuj, and Ann Shivers-McNair. "“Wayfinding” through the AI wilderness: Mapping rhetorics of ChatGPT prompt writing on X (formerly Twitter) to promote critical AI literacies." Computers and Composition 74 (December 2024): 102882. http://dx.doi.org/10.1016/j.compcom.2024.102882.

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Anuik, Jonathan. "Classroom Incivility Going Viral on Social Media: One Professor’s Encounters." Journal of Contemporary Issues in Education 19, no. 2 (December 9, 2024): 11–36. https://doi.org/10.20355/jcie29601.

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The advent of social media expands the domain of the standard classroom. The outcome is that incidents of conflict, also known as classroom incivility, exist not only within the conventional classroom. They can take place on social media platforms. In this paper, the author draws on his experience with a complaint about an assignment in one of his classes that surfaced on X (formerly Twitter), feedback on the assignment that took the shape of a story on Instagram, and his response to the activity in the two venues. He draws on the concept of classroom incivility and reflects on how this term, named to describe conflict in in-person classrooms, is relevant on social media platforms.

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Teano, Anthony L., Ashley Scott, Cassandra Gipson, Marilyn Albert, and Corinne Pettigrew. "Social Media Programs for Outreach and Recruitment Supporting Aging and Alzheimer Disease and Related Dementias Research: Longitudinal Descriptive Study." JMIR Aging 7 (July 9, 2024): e51520. http://dx.doi.org/10.2196/51520.

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Background Social media may be a useful method for research centers to deliver health messages, increase their visibility in the local community, and recruit study participants. Sharing examples of social media–based community outreach and educational programs, and evaluating their outcomes in this setting, is important for understanding whether these efforts have a measurable impact. Objective The aim of this study is to describe one center’s social media activities for community education on topics related to aging, memory loss, and Alzheimer disease and related dementias, and provide metrics related to recruitment into clinical research studies. Methods Several social media platforms were used, including Facebook, X (formerly Twitter), and YouTube. Objective assessments quantified monthly, based on each platform’s native dashboard, included the number of followers, number of posts, post reach and engagement, post impressions, and video views. The number of participants volunteering for research during this period was additionally tracked using a secure database. Educational material posted to social media most frequently included content developed by center staff, content from partner organizations, and news articles or resources featuring center researchers. Multiple educational programs were developed, including social media series, web-based talks, Twitter chats, and webinars. In more recent years, Facebook content was occasionally boosted to increase visibility in the local geographical region. Results Up to 4 years of page metrics demonstrated continuing growth in reaching social media audiences, as indicated by increases over time in the numbers of likes or followers on Facebook and X/Twitter and views of YouTube videos (growth trajectories). While Facebook reach and X/Twitter impression rates were reasonable, Facebook engagement rates were more modest. Months that included boosted Facebook posts resulted in a greater change in page followers and page likes, and higher reach and engagement rates (all P≤.002). Recruitment of participants into center-affiliated research studies increased during this time frame, particularly in response to boosted Facebook posts. Conclusions These data demonstrate that social media activities can provide meaningful community educational opportunities focused on Alzheimer disease and related dementias and have a measurable impact on the recruitment of participants into research studies. Additionally, this study highlights the importance of tracking outreach program outcomes for evaluating return on investment.

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Furini, Marco. "X as a Passive Sensor to Identify Opinion Leaders: A Novel Method for Balancing Visibility and Community Engagement." Sensors 24, no. 2 (January 18, 2024): 610. http://dx.doi.org/10.3390/s24020610.

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The identification of opinion leaders is a matter of great significance for companies and authorities, as these individuals are able to shape the opinions and attitudes of entire societies. In this paper, we consider X (formerly Twitter) as a passive sensor to identify opinion leaders. Given the unreliability of the traditional follower count metric due to the presence of fake accounts and farm bots, our approach combines the measures of visibility and community engagement to identify these influential individuals. Through an experimental evaluation involving approximately 4 million tweets, we showed two important findings: (i) relying solely on follower count or post frequency is inadequate for accurately identifying opinion leaders, (ii) opinion leaders are able to build community and gain visibility around specific themes. The results showed the benefits of using X as a passive sensor to identify opinion leaders, as the proposed method offers substantial advantages for those who are involved in social media communication strategies, including political campaigns, brand monitoring, and policymaking.

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Bozdag, Utku. "Framing displaced persons : An analysis of Turkish media’s use of migration metaphors on Twitter." Intersections 10, no. 1 (2024): 117–36. http://dx.doi.org/10.17356/ieejsp.v10i1.1189.

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In recent years, social media has been recognized as instrumental in shaping the discourse around displaced persons, particularly through the power of metaphorical framing. Given that online communication can lead to real-world consequences for individuals, X (formerly known as Twitter) now stands out as a crucial platform for discussing migration issues in Turkey. However, while Twitter holds significant sway over public discourse in Turkey, there remains a research gap concerning its role in migration-related metaphorical framing. This study, employing critical metaphor analysis (CMA), delves into the metaphorical representations associated with the terms göçmen (‘migrant’), sığınmacı (‘asylum-seeker’), and mülteci (‘refugee’) in the tweets of four major Turkish media outlets: Hürriyet, Haber Türk, Sözcü, and Cumhuriyet. The findings reveal a predominant negative framing of all three terms, with göçmen and sığınmacı often equated with crime and mülteci with objectification. Also, the political inclination of the media outlets played a role, with those aligned closer to the Turkish government showcasing fewer negative frames. In conclusion, the study highlights the profound impact of media’s metaphorical framing, revealing its capacity to color public perceptions about displaced populations, a phenomenon accentuated by the specific terms chosen and the media’s political leanings.

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Katalinić, Josip, Ivan Dunđer, and Sanja Seljan. "Unraveling the Nuclear Debate: Insights Through Clustering of Tweets." Electronics 13, no. 21 (October 23, 2024): 4159. http://dx.doi.org/10.3390/electronics13214159.

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The perception of nuclear power, while central to energy policy and sustainability endeavors, remains a subject of considerable debate, in which some claim that the expansion of nuclear technology poses threats to global security, while others argue that its access should be shared for development and energy purposes. In this study, a total of 11,256 tweets were gathered over a three-month period using a keyword-based approach through the Twitter Standard Search API, focusing on terms related to nuclear energy. The k-means clustering algorithm was employed to analyze tweets with the aim of determining the underlying sentiments and perspectives within the public domain, while t-SNE was used for visualizing cluster separation. The results show distinct clusters reflecting various viewpoints on nuclear power, with 71.94% of tweets being neutral, 14.64% supportive, and 13.42% negative. This study also identifies a subset of users who appear to be seeking unbiased information, signaling an opportunity for educational outreach. By leveraging the immediacy and pervasiveness of X (formerly known as Twitter), this research provides a timely snapshot of the prevailing attitudes toward nuclear power and offers insights for policymakers, educators, and industry stakeholders.

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Dracewicz, Weronika, and Mariusz Sepczuk. "Detecting Fake Accounts on Social Media Portals—The X Portal Case Study." Electronics 13, no. 13 (June 28, 2024): 2542. http://dx.doi.org/10.3390/electronics13132542.

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Today, social media are an integral part of everyone’s life. In addition to their traditional uses of creating and maintaining relationships, they are also used to exchange views and all kinds of content. With the development of these media, they have become the target of various attacks. In particular, the existence of fake accounts on social networks can lead to many types of abuse, such as phishing or disinformation, which is a big challenge nowadays. In this work, we present a solution for detecting fake accounts on the X portal (formerly Twitter). The main goal behind the developed solution was to use images of X portal accounts and perform image classification using machine learning. As a result, it was possible to detect real and fake accounts and indicate the type of a particular account. The created solution was trained and tested on an adequately prepared dataset containing 15,000 generated accounts and real X portal accounts. The CNN model performing with accuracy above 92% and manual test results allow us to conclude that the proposed solution can be used to detect false accounts on the X portal.

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Li, Yuan, and Ainsley Sprayberry. "People's Voice: Exploring Discussion Themes on Generative AI and Libraries on X." Proceedings of the Association for Information Science and Technology 61, no. 1 (October 2024): 992–94. http://dx.doi.org/10.1002/pra2.1164.

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ABSTRACTThe launch of ChatGPT and other Generative Artificial Intelligence (GenAI) models has significantly influenced people's perceptions of AI, making its impact a widely discussed topic in various areas. In Library and Information Science (LIS), researchers experience the opportunities and challenges presented by GenAI, such as using the rich text from books and digital collections as training data for GenAI and enabling quick summarization and reference consultation for patrons. Meanwhile, concerns have arisen about the generation of fake information and nonexistent text by GenAI and their potential effects on users. This project explores the themes in public discussions regarding the impact of GenAI on libraries. To explore public opinions about the impact of GenAI in libraries, we collected over 50,000 posts from X (formerly Twitter) between November 1, 2022, and May 6, 2024. We manually sampled 1,000 posts focusing on GenAI in the context of libraries. We conducted content analysis to identify the key themes and relationships among these themes. This poster presents our initial findings on different themes and highlights the trends in public opinion. Our analysis provides a curated dataset for training APIs for future investigation.

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Carius, Ana Carolina, and Maxwel Pinto Vieira. "Influencers and Influence: the dissemination of information in times of fake news and post-truth." OBSERVATÓRIO DE LA ECONOMÍA LATINOAMERICANA 22, no. 2 (February 19, 2024): e3304. http://dx.doi.org/10.55905/oelv22n2-140.

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The end of 2023 was marked, on Brazilian social networks, by a tragedy: the death of a 22-year-old young woman, influenced by a relatively new phenomenon in the social spectrum, the so-called fake news. Given the repercussion of the fact, this work has as its object of study the phenomenon of information dissemination on social networks, under the scope of social network X (formerly Twitter). Based on an information diffusion model, this research has, as a general objective, to evaluate the dissemination of information on social network. Specific objectives include investigating the relevance of the information dissemination model to the issue of disseminating fake news; test the information diffusion model with experimental data obtained from the social network X and validate the results obtained in numerical simulations arising from the information diffusion model. It is concluded, therefore, that the lower the estimated real reach of a post, the longer the information dissemination process will take, which would justify the introduction of joint work between different profiles in partnership with bots to increase the engagement of a post.

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Fachrurrozy Nurqoulby, Amalia Anjani Arifiyanti, and Dhian Satria Yudha Kartika. "Analysis Sentiment Of Users Internet Service Providers In Indonesia On Social Media X Using Support Vector Machine." Data Science: Journal of Computing and Applied Informatics 8, no. 2 (July 31, 2024): 88–95. http://dx.doi.org/10.32734/jocai.v8.i2-16317.

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Various internet service providers are starting to appear in Indonesia, they are competing to provide attractive offers to attract customers. Through social media, someone can find out opinions about whether internet service providers provide services as offered. X, formerly known as Twitter, is a social media platform where people can give their opinions in the form of posts. Various opinions were expressed by the public, ranging from positive, neutral, to negative. This research aims to create a post classification model regarding users of internet service providers into three sentiment classes, namely positive, neutral and negative. The model is created through several stages, such as data retrieval, data labeling, data preprocessing, data division, term weighting, and creating a classification model using the Support Vector Machine algorithm. The results of this research show that the SVM model with a Linear kernel obtained the highest accuracy of 83% compared to the RBF kernel SVM and Polynomial kernel SVM, with an F1-score of 90% for the negative class, 66% for the neutral class, and 65% for the positive class.

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Adewale Omipidan, Ismail, Maranatha Morenike Olanrewaju, Janey Favour Wilson, Omowale Taofeek Adelabu, Adeola Oluwatoyosi Ajala, Ayomide John Fajoye, Christianah Opeyemi Onigbinde, and Dupe Sekinat Adeleke-Sola. "The Role of Social Media in Shaping Public Discourse on Government Policies in Nigeria: A Discourse of the Nigerian X (Formerly Twitter) Space." Journal of African Films and Diaspora Studies 7, no. 3 (September 1, 2024): 65–85. http://dx.doi.org/10.31920/2516-2713/2024/7n3a4.

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Hussna, Asma Ul, Md Golam Rabiul Alam, Risul Islam, Bader Fahad Alkhamees, Mohammad Mehedi Hassan, and Md Zia Uddin. "Dissecting the infodemic: An in-depth analysis of COVID-19 misinformation detection on X (formerly Twitter) utilizing machine learning and deep learning techniques." Heliyon 10, no. 18 (September 2024): e37760. http://dx.doi.org/10.1016/j.heliyon.2024.e37760.

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Thufailah, Karina Khansa, Biyan Mahesa Albianazwa, and Deliana Maulida Ahsanti. "Sentiment Analysis of Public Opinion on Public Housing Savings Policy (Tapera) on Social Media “X”." Tamalanrea: Journal of Government and Development (JGD) 1, no. 2 (August 5, 2024): 1–11. http://dx.doi.org/10.69816/jgd.v1i2.35841.

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This study aims to analyze public sentiment towards the People's Housing Savings (TAPERA) policy on social media platform X (formerly Twitter). Responses to the Tapera policy can reflect the level of public trust in the government and the agency that manages Tapera. Sentiment analysis can provide an overview of this trust. This study uses a quantitative method with sentiment analysis techniques to measure public reactions based on comments on platform X. Data were collected from several comments related to the TAPERA policy and analyzed using the confusion matrix validation technique to ensure the accuracy of the results. The results showed that more than 90% of public sentiment towards the TAPERA policy was negative. This negative sentiment was mostly triggered by public concerns about salary cuts that were considered burdensome and low trust in the government's ability to manage the funds in a transparent and accountable manner. These findings emphasize the importance of transparency, effective communication, and public participation in the formulation of public policy. These findings are expected to provide insight for stakeholders in formulating policies that are more responsive to the needs and aspirations of the community.

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Toapanta Bernabe, Mariuxi, Miguel Angel Garcia-Cumbreras, and L. Alfonso Urena-Lopez. "Fake News Detection and Fact Checking in X posts from Ecuador Chequea and Ecuador Verifica using Spanish Language Models." Revista Tecnológica - ESPOL 36, no. 2 (December 30, 2024): 158–73. https://doi.org/10.37815/rte.v36n2.1219.

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Currently, verifying news content before its dissemination poses a significant challenge due to the rapidity with which it spreads and the ease of replication. These factors contribute to the proliferation of fake news. Collaborative initiatives like Duke Reporters' Lab and the International Fact-Checking Network (IFCN) have been established to enhance the accuracy of fact-checking to combat various forms of disinformation. The accredited fact-checking platforms in Ecuador are Ecuador Chequea and Ecuador Verifica. This paper details the outcomes from five transformer-based models, namely BETO, MarIA, RoBERTuito, BERTuit, and BERTin, for classifying fake news in Spanish. The rating system of Ecuador Chequea and Ecuador Verifica validated the news gathered from these platforms' accounts on the social network X (Formally known as Twitter), including X posts generated between January 2020 and March 2024. The findings validate that in terms of accuracy, recall, precision, and F1 score, the MarIA language model outperforms other Spanish-based models such as BERTin, RoBERTuito, BETO, and BERTuit.

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Ajetunmobi, Umar Olansile, Akinkunmi Ibrahim Oseni, Abdurrahman Bello Onifade, and Teslim Abiodun Adegboyega. "Fear of Expansion and Domination: Toxic and Ethnic Frames Amidst Call to #SayNoToRUGA on Nigerian Twittersphere." Media Watch 15, no. 2 (May 2024): 246–66. http://dx.doi.org/10.1177/09760911241230708.

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Nigerians have, over the years, lived with mutual distrust, often escalated by ethnoreligious sentiments and sectional profiling. Government policies, for example, are at times seen through the lens of the ethnic background or sectional affiliation of the president at any given time. This study examined the kinds of frames Nigerian X (formerly Twitter) users adopted during the #SayNoToRUGA movement on X. It also investigated how the digital movement predicted the polarisation of Nigerians across ethnic and sectional divides. Through a summative content analysis of 145 purposively selected tweets of #SayNoToRUGA and propositions of framing theory, the study found four dominant frames: toxic discourse, ethnic profiling, call to social action and misinformation. Findings also revealed that toxic discourse comprised more abusive tweets alongside tweets that unjustifiably accused the Fulani tribe and constructed identities for it and its people. Fear of domination and expansion of the tribe also fuelled the level of toxic discourse on #SayNoToRUGA. The findings also predicted a significant polarisation of Nigerian X users on the digital movement across ethnic and sectional divides. Therefore, it recommends that relevant government agencies [e.g., National Orientation Agency (NOA), communication and culture ministry] host regular cultural and ethnoreligious literacy skills on X Spaces. Through Spaces, they can also consult Nigerians, with each region making its inputs on sensitive national policies such as Rural Grazing Area (RUGA).

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Jiménez-Hidalgo, Pedro Jesús, Carlos Ruiz-Núñez, Beatriz Jiménez-Gómez, Sergio Segado-Fernández, Carlos Santiago Romero-Magdalena, Fidel López-Espuela, and Ivan Herrera-Peco. "Spanish Healthcare Institutions and Their Role in Social Media-Driven Influenza Vaccination Campaigns: A Comprehensive Analysis of X." Social Sciences 13, no. 10 (October 18, 2024): 557. http://dx.doi.org/10.3390/socsci13100557.

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Social media plays a crucial role in health information dissemination, yet it also raises concerns about misinformation. This study examines the role of Spanish health centers in promoting influenza vaccination on social networks, particularly X (formerly Twitter), during the 2023–2024 campaign. An observational, retrospective study analyzed the activity of 832 Spanish health centers on X. Data collection focused on the existence of official accounts, follower engagement and the nature of messages posted. Metrics were obtained using X Analytics, and statistical analysis was performed using JAMOVI v2.4 software. Of the 832 centers, 607 had readable X accounts. Collective accounts were more prevalent (351) than individual ones (239). Collective accounts had significantly more followers and posts but showed less engagement compared to individual accounts. The most followed accounts belonged to public entities, like the Community of Madrid and private organizations, such as Sanitas. An analysis of the vaccination campaign revealed that most posts had a political focus with limited health information, resulting in minimal interaction with the public. As a conclusion, the study highlights the fragmented nature of health communication through social networks in Spain. Despite the higher visibility of collective accounts, their engagement with the public is low, often due to the political nature of posts. A unified national strategy is essential for enhancing public health communication, focusing on interactive and relevant content.

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Akinmusuyi, Samuel. "“STOP THIS NONSENSE, HARRY!”: An analysis of impoliteness strategies in cyberbullying commentary targeting Harry Maguire." Journal of Languages, Linguistics and Literary Studies 3, no. 4 (December 29, 2023): 183–92. http://dx.doi.org/10.57040/jllls.v3i4.541.

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Cyberbullying encompasses a range of negative online behaviours, including harassment, threats, and insults, which can significantly impact individuals’ mental and emotional wellbeing. This study aims to examine the nature and prevalence of impoliteness strategies within cyberbullying commentary targeting Harry Maguire, a prominent figure in the football world. The data consists of 100 purposively selected comments showcasing impoliteness, extracted from the comments section of a post by Maguire, on his official X platform (formerly Twitter) account dated 7th November 2021. Culpeper’s (2005) Model of Impoliteness serves as the theoretical framework while a mixed method approach is adopted in this study. Out of the five strategies proposed by Culpeper (2005), this study discovers four strategies present in the selected cyberbullying comments. The findings show that Positive Impoliteness (39.6%) is the most frequently employed strategy, and this is followed by Bald on Record Impoliteness, which represents 27.7% of the total comments. The study concludes that within the digital sports community on the X platform, fans frequently employ strategies of positive impoliteness and bald-on-record impoliteness as a means to challenge the face wants of players, such as Harry Maguire, particularly when their performances are below expectation.

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Howard, Conner, Ryan McIntire, J. Michael Anderson, Carter Stewart, Haddon McIntosh, James Cornwell, and Kim Barron. "The top sports medicine influencers on X (formerly Twitter)." Journal of Sports Sciences, September 18, 2023, 1–6. http://dx.doi.org/10.1080/02640414.2023.2259723.

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Allen, Matthew R., Nimit Desai, Aiden Namazi, Eric Leas, Mark Dredze, Davey M. Smith, and John W. Ayers. "Characteristics of X (Formerly Twitter) Community Notes Addressing COVID-19 Vaccine Misinformation." JAMA, April 24, 2024. http://dx.doi.org/10.1001/jama.2024.4800.

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Kumari, Smriti, Anamika Sharma, Amit Chhabra, Ankit Gupta, Sarabjeet Singh, and Ravi Verma. "Analysing public sentiment towards robotic surgery: an X (formerly Twitter) based study." Social Network Analysis and Mining 14, no. 1 (March 16, 2024). http://dx.doi.org/10.1007/s13278-024-01226-9.

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Journal articles on the topic 'X (formely Twitter)'

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