Gotowa bibliografia na temat „X (formely Twitter)”

Loneliness can be studied through social media and web data to gain insights into the dynamic phenomenon. In this paper, we present our proposed framework through a case study on how to deploy various social media and online data sources to study loneliness comprehensively. The framework is important to understand loneliness from data perspective available online and to complement the theoretical and psychosocial understanding of loneliness. The data on loneliness is gathered through surveys and self-reporting mechanisms. This requires complementing the dynamic and vast data available on the web and through social media. Our results found that tools like Google Trend and News Analysis can give the starting point to explore loneliness in a particular region. Tools like X (formerly Twitter), Reddit, and other social media can give behavioral data on loneliness which can be analyzed through sentiment analysis and other social intelligence analysis. The framework’s utility lies in its potential to inform policies, interventions, and initiatives addressing loneliness through data. However, it is crucial to acknowledge limitations, such as data availability, biases in user-generated content, and ethical considerations.

Abstract International organizations’ bureaucracies are important facilitators and authorities of global governance that increasingly use digital means of communication—such as websites, email, social media, or mobile apps—to address their audiences directly. How do they deal with the opportunities and challenges of such ‘digital diplomacy’? What are the consequences of digital diplomacy regarding the content communicated by international bureaucracies and beyond? This chapter attempts to demonstrate the significance of recent research for providing conclusive answers to these questions as well as to demonstrate the need for further exploration. It first illustrates the current practices of international bureaucracies’ digital diplomacy by focusing on social media such as X (formerly known as Twitter) and Facebook in a quantitative perspective, illustrated with recent examples of their use. Second it discusses motivational factors and conducive conditions that help to explain why international bureaucracies have turned to, and increasingly employ, means of digital diplomacy. While the first and second parts of the chapter focus mainly on the opportunities of digital diplomacy, the third part goes full circle by turning to a couple of its challenges—organized hypocrisy, pluralization, acceleration, selectivity, personalization, and polarization—of which little is yet known, but that deserve more attention by scholars, practitioners, and stakeholders of global governance alike.

Social media channels have become omnipresent tools for communicating, sharing knowledge, and establishing communities, but also places where all sorts of hate speech comes to the surface. This chapter contributes to the body of work on online hate speech towards sexual orientation minorities and takes some initial steps towards a quantitative variationist sociolinguistic study of homophobic language in Italian social media. By exploring the different lexicalisations (i.e. near-synonyms) available in Italian for expressing or referring to the concept of "homosexual man" on X (formerly known as Twitter), this study provides both a descriptive and a methodological contribution. For this purpose, we created a dataset of 3000 manually annotated tweets in which at least one of the 33 lexicalisations of the concept "homosexual man" was recorded, and which we annotated for linguistic and stylistic factors that favour the perception of an expression as offensive. Our study shows that the interaction of lexical variation and explicit and implicit contextual cues, such as irony and dialect use, is significant in determining the degree of offensiveness of tweets. Furthermore, this study provides further evidence in favour of using social media as a laboratory for mapping language variation on a large scale, and for reflecting on the refinement of semi-automatic annotation of linguistic, stylistic, and social variables in written social media language.

Abstract This chapter addresses the regulatory framework that governs unlawful online content, including harassment, racism, extremism, and other illegal online content, with a special focus on cyberviolence and online ‘hate speech’—including non-consensual nudity sharing (commonly referred to as revenge porn)—against women. In addition to independent efforts undertaken by Internet Service Providers to combat online criminality by means of self-imposed Community Guidelines or Terms of Use, regulations have been introduced in order to more effectively combat hate and incitement as well as extremism on the Internet. In this respect, this chapter provides an overview on international conventions and action plans, recommendations, resolutions, EU directives, guidelines, and regulations––particularly on the recent enactment of the EU’s Digital Service Act (DSA)––as well as on Germany’s Act to Improve Enforcement of the Law in Social Networks (Network Enforcement Act, NetzDG), as an example of a national regulatory model. NetzDG requires Internet Service Providers with more than two million registered users in Germany (e.g. those on the scale of Google, Facebook, YouTube, X (formerly, Twitter), and Instagram) to set up a transparent complaints management system as private actors in order to efficiently handle complaints about illegal content as defined in NetzDG. The Act imposes on Internet Service Providers the responsibility of balancing human rights and fundamental freedoms against each other, thereby changing the role of these private actors—against their will—and making them gatekeepers at the threshold of fundamental and human rights.

This research aims to know the nature of the use of media discourse as an effective tool in developing digital diplomacy. This study is based on descriptive studies, using a survey approach in the form of a quantitative analytical survey to analyze the contents of media discourse on the pages of social media sites of the political elite in Iraq, which is represented by (published tweets By Iraqi personalities), as a deliberate, non-random sample in the period (10-1-2023) to (12-31-2023) on their official pages in the (X) network (formerly Twitter, which is used in digital diplomacy. The study reached a set of results, the most important of which are: that Muqtada al-Sadr’s page comes in first place among Twitter pages (X) compared to other Iraqi political figures, and that Al-Sudani’s page comes in first place in terms of the number of tweets published on Twitter pages (X) And that Muqtada al-Sadr’s page comes in first place in terms of the number of likes, comments, reposts, and views of tweets published on Twitter pages (X) compared to other Iraqi political figures, and that the Iraqi Prime Minister’s page comes in first place in terms of the focal points for tweets published by Iraqi political figures on pages Twitter (X). Al-Sudani’s page also comes in first place in terms of the positions of tweets published by Iraqi political figures on Twitter (X) pages compared to other Iraqi political figures. Finally, the Iraqi Prime Minister’s page comes in first place in terms of the positions of published tweets by Iraqi political figures. On Twitter pages (X), compared to other Iraqi political figures.

In a time when social media information is a valuable resource for gaining insights, the COVID-19 pandemic has released a flood of public sentiment, abundant with unstructured text data. This paper introduces CovBERT, a novel adaptation of the BERT model, specifically honed for the nuanced analysis of COVID-19-related discourse on X (formerly Twitter). CovBERT stands out by incorporating a bespoke vocabulary, meticulously curated from pandemic-centric tweets, resulting in a remarkable leap in sentiment analysis accuracy—from the baseline 72\% to an impressive 78.64\%. This paper not only presents a detailed comparison of CovBERT with the standard BERT model but also juxtaposes it against traditional machine learning approaches, showcasing its superior proficiency in decoding complex emotional undercurrents in social media data. Furthermore, the integration of geolocation analysis pipeline adds another layer of depth, offering a panoramic view of global sentiment trends.

Spreading electoral propaganda using Online Social Networks (OSNs) during elections is an important problem and novel approaches are necessary to mitigate its effects. The lack of automatic electoral propaganda detection supports candidates which makes true digital podiums have emerged for candidates to spread their ideas, fight opponents, and ask for votes during the pre-electoral period. In Brazil, it is prohibited by law to declare candidacy in a political election and to make any (explicit or implicit) request to vote ahead of time. In this context, this work presents a system named Campanha Eleitoral Legal to help the detection of this type of propaganda on X (formerly Twitter) adopted by Ministério Público de Minas Gerais (MPMG). Our system is able to collect, categorize, and highlight posts that contain a high probability of being electoral propaganda. Thus, this system can be great tool for Brazilian authorities

We used Alexandria Digital, a world leading disinformation detection technology, to analyse almost a million posts on X (formerly known as Twitter) and Reddit comments during the first Republican primary debate and counterprogrammed Tucker Carlson and Donald Trump interview on the 23rd of August. What we did: • Collected 949,259 posts from the platform X, formerly known as Twitter. These posts were collected if they contained one of 11 relevant hashtags or keywords and were posted between 8:45pm and 11:15pm EST on 23rd August 2023. • Collected 20,549 comments from two separate Reddit threads. Both were discussion threads dedicated to the first Republican primary Debate and the Tucker Carlson and Donald Trump interview from r/Conservative and r/politics. • This methodology allowed us to capture narratives and conduct analysis of coordinated behaviour that occurred immediately before, during, and after the Republican primary debate and the airing of the Tucker Carlson interview of Donald Trump. What we found: • A coordinated network of over 1200 accounts promoting the conspiracy theory that Donald Trump won the 2020 United States presidential election that received over 3 million impressions on the platform X; • A sprawling bot network consisting of 1,305 unique accounts with a variety of clusters; • Some of the largest clusters were coordinated troll networks in support of Donald Trump; a coordinated network of misleading news outlets, and a clickbait Pro-Trump bot network. • No coordinated activity was found on Reddit during the Republican Primary Debate or in discussion of the Tucker Carlson and Donald Trump interview. What does this mean? • X is flooded with platform manipulation of various kinds, is not doing enough to moderate content, and has no clear strategy for dealing with political disinformation. • A haven for disinformation. While pre-Musk Twitter previously managed to moderate harmful conspiracy theories such as QAnon, X is now a safe space for conspiracy theorists and political disinformation. • That no evidence of coordinated influence activity was found on Reddit suggests the extensive rules and moderation either prevented or removed coordinated activity from the platform. • Worrying trends. Given the prevalence of mis- and disinformation during the debate and interview, the leadup to the US 2024 Presidential Election is likely to witness a surge of information disorder on the platform. • Trump is back. The reinstatement of Donald Trump’s X account has emboldened conspiracy theorists and the far right, who are interpreting this as a sign that the reason why Trump was suspended (incitement to violence) validates election fraud disinformation and activism. • Anything goes. The lack of a freely available Twitter Application Programming Interface (API) means that researchers, journalists, and regulators cannot monitor disinformation on X and hold the platform to account.