Project description:The 2024 Taiwanese Presidential Election is not just a critical geopolitical event, it also engages with themes of alternative candidacy, foreign policy, and affective polarization. At one point, a four-candidate race had emerged in a traditionally bipartisan election, with alternative candidates disrupting the dichotomy of Chinese vs. Taiwanese identity. Leveraging 911,510 posts and 101,600,047 engagements on social media, we analyze user discourse and engagement. First, we find traditional candidates derive more engagement on foreign policy and geopolitical issues, alternative candidates on domestic issues. Additionally, virality is generated by affective reasons, although in-group references generate more engagement than out-group references. Lastly, a puzzle is revealed where alternative candidates draw more homogeneous attention from national identity groups. Results suggest alternative candidacy can be generated by both positive and negative comparisons rooted in national identity.
Project description:This study promotes the news repertoire framework as an analytical approach best suited for studying news engagement on social media (SM), considering its multifaceted nature. To demonstrate the theoretical benefits of this proposal, the study seeks to (1) identify user profiles based on SM news viewing and sharing, and news consumption on other platforms; (2) determine profile predictors; and (3) evaluate their possible outcomes. A panel study (N = 1786) demonstrated the emergence of identifiable profiles, attributed to differences in SM use and political interest. In addition, profiles embodied different effects on political participation over time. A second study (N = 86) was conducted thereafter, in which users' Facebook news feed use was analyzed to determine differences in news supply according to profiles. Findings that could not have been achieved using the more common unidimensional news consumption methods are discussed in light of new theoretical gains provided by the repertoire approach.
Project description:As social media becomes a key channel for news consumption and sharing, proliferating partisan and mainstream news sources must increasingly compete for users' attention. While affective qualities of news content may promote engagement, it is not clear whether news source bias influences affective content production or virality, or whether any differences have changed over time. We analyzed the sentiment of ~30 million posts (on twitter.com) from 182 U.S. news sources that ranged from extreme left to right bias over the course of a decade (2011-2020). Biased news sources (on both left and right) produced more high arousal negative affective content than balanced sources. High arousal negative content also increased reposting for biased versus balanced sources. The combination of increased prevalence and virality for high arousal negative affective content was not evident for other types of affective content. Over a decade, the virality of high arousal negative affective content also increased, particularly in balanced news sources, and in posts about politics. Together, these findings reveal that high arousal negative affective content may promote the spread of news from biased sources, and conversely imply that sentiment analysis tools might help social media users to counteract these trends.
Project description:How does the credibility we attribute to media sources influence our opinions and judgments derived from news? Participants read headlines about the social behavior of depicted unfamiliar persons from websites of trusted or distrusted well-known German news media. As a consequence, persons paired with negative or positive headlines were judged more negative or positive than persons associated with neutral information independent of source credibility. Likewise, electrophysiological signatures of slow and controlled evaluative brain activity revealed a dominant influence of emotional headline contents regardless of credibility. Modulations of earlier brain responses associated with arousal and reflexive emotional processing show an effect of negative news and suggest that distrusted sources may even enhance the impact of negative headlines. These findings demonstrate that though we may have distinct perceptions about the credibility of media sources, information processing and social judgments rely on the emotional content of headlines, even when they stem from sources we distrust.
Project description:We analyze social media activity during one of the largest protest mobilizations in US history to examine ideological asymmetries in the posting of news content. Using an unprecedented combination of four datasets (tracking offline protests, social media activity, web browsing, and the reliability of news sources), we show that there is no evidence of unreliable sources having any prominent visibility during the protest period, but we do identify asymmetries in the ideological slant of the sources shared on social media, with a clear bias towards right-leaning domains. These results support the "amplification of the right" thesis, which points to the structural conditions (social and technological) that lead to higher visibility of content with a partisan bent towards the right. Our findings provide evidence that right-leaning sources gain more visibility on social media and reveal that ideological asymmetries manifest themselves even in the context of movements with progressive goals.
Project description:Prior research demonstrates that news-related social media posts using negative language are re-posted more, rewarding users who produce negative content. We investigate whether negative material from external news sites is also introduced to social media through more user posts, offering comparable incentives for journalists to adopt a negative tone. Data from four US and UK news sites (95,282 articles) and two social media platforms (579,182,075 posts on Facebook and Twitter, now X) show social media users are 1.91 times more likely to share links to negative news articles. The impact of negativity varies by news site and social media platform and, for political articles, is moderated by topic focus, with users showing a greater inclination to share negative articles referring to opposing political groups. Additionally, negativity amplifies news dissemination on social media to a greater extent when accounting for the re-sharing of user posts containing article links. These findings suggest a higher prevalence of negatively toned articles on Facebook and Twitter compared to online news sites. Further, should journalists respond to the incentives created by the heightened sharing of negative articles to social media platforms, this could even increase negative news exposure for those who do not use social media.
Project description:Deceased public figures are often said to live on in collective memory. We quantify this phenomenon by tracking mentions of 2,362 public figures in English-language online news and social media (Twitter) 1 y before and after death. We measure the sharp spike and rapid decay of attention following death and model collective memory as a composition of communicative and cultural memory. Clustering reveals four patterns of postmortem memory, and regression analysis shows that boosts in media attention are largest for premortem popular anglophones who died a young, unnatural death; that long-term boosts are smallest for leaders and largest for artists; and that, while both the news and Twitter are triggered by young and unnatural deaths, the news additionally curates collective memory when old persons or leaders die. Overall, we illuminate the age-old question of who is remembered by society, and the distinct roles of news and social media in collective memory formation.
Project description:Topic modeling is an active research area with several unanswered questions. The focus of recent research in this area is on the use of a vector embedding representation of the input text with both generative and evolutionary topic modeling techniques. Unfortunately, it is hard to compare different techniques when the underlying data and preprocessing steps that were used to develop the models are not available. This paper presents two secondary datasets that can help address this gap. These datasets are derived from two primary datasets. The first consists of 8145 posts from the r/Cancer health forum and the second consists of 18,294 messages submitted to 20 different news groups. The same preprocessing procedure is applied to both datasets by removing punctuation, stop words and high frequency words. Each dataset is then clustered using three different topic modeling techniques: pPSO, ETM and NVDM and three topic numbers: 10, 20, 30. In addition, for pPSO two text embeddings representation are considered: sBERT and Skipgram. The secondary datasets were originally developed in support of a comparative analysis of the aforementioned topic modeling techniques in a study titled "Comparing PSO-based Clustering over Contextual Vector Embeddings to Modern Topic Modeling" submitted to the Journal of Information Processing and Management. The present paper provides a detailed description of the two secondary datasets including the unique identifier that can be used to retrieve the original documents, the pre-processing scripts, the topic keywords generated by the three topic modeling techniques with varying topic numbers and embedding representations. As such, the datasets allow direct comparison with other topic modeling techniques. To further facilitate this process, the algorithm underlying the evolutionary topic modeling technique, pPSO, proposed by the authors is also provided.
Project description:Highlights • Social media, fake news, and COVID-19.• Misinformation on social media has fuelled panic regarding the COVID-19.• Altruism is the strongest predictor of fake news sharing on COVID-19.• Socialization, information seeking and pass time predict fake news sharing.• Entertainment is not associated with sharing fake news on COVID-19. Fake news dissemination on COVID-19 has increased in recent months, and the factors that lead to the sharing of this misinformation is less well studied. Therefore, this paper describes the result of a Nigerian sample (n = 385) regarding the proliferation of fake news on COVID-19. The fake news phenomenon was studied using the Uses and Gratification framework, which was extended by an “altruism” motivation. The data were analysed with Partial Least Squares (PLS) to determine the effects of six variables on the outcome of fake news sharing. Our results showed that altruism was the most significant factor that predicted fake news sharing of COVID-19. We also found that social media users’ motivations for information sharing, socialisation, information seeking and pass time predicted the sharing of false information about COVID-19. In contrast, no significant association was found for entertainment motivation. We concluded with some theoretical and practical implications.
Project description:BackgroundSocial media is widely used as a source of news and information regarding COVID-19. However, the abundance of misinformation on social media platforms has raised concerns regarding the spreading infodemic. Accordingly, many have questioned the utility and impact of social media news use on users' engagement with (mis)information.ObjectiveThis study offers a conceptual framework for how social media news use influences COVID-19 misinformation engagement. More specifically, we examined how news consumption on social media leads to COVID-19 misinformation sharing by inducing belief in such misinformation. We further explored if the effects of social media news use on COVID-19 misinformation engagement depend on individual differences in cognition and personality traits.MethodsWe used data from an online survey panel administered by a survey agency (Qualtrics) in Singapore. The survey was conducted in March 2022, and 500 respondents answered the survey. All participants were older than 21 years and provided consent before taking part in the study. We used linear regression, mediation, and moderated mediation analyses to explore the proposed relationships between social media news use, cognitive ability, personality traits, and COVID-19 misinformation belief and sharing intentions.ResultsThe results suggested that those who frequently used social media for news consumption were more likely to believe COVID-19 misinformation and share it on social media. Further probing the mechanism suggested that social media news use translated into sharing intent via the perceived accuracy of misinformation. Simply put, social media news users shared COVID-19 misinformation because they believed it to be accurate. We also found that those with high levels of extraversion than those with low levels were more likely to perceive the misinformation to be accurate and share it. Those with high levels of neuroticism and openness than those with low levels were also likely to perceive the misinformation to be accurate. Finally, it was observed that personality traits did not significantly influence misinformation sharing at higher levels of cognitive ability, but low cognitive users largely drove misinformation sharing across personality traits.ConclusionsThe reliance on social media platforms for news consumption during the COVID-19 pandemic has amplified, with dire consequences for misinformation sharing. This study shows that increased social media news consumption is associated with believing and sharing COVID-19 misinformation, with low cognitive users being the most vulnerable. We offer recommendations to newsmakers, social media moderators, and policymakers toward efforts in limiting COVID-19 misinformation propagation and safeguarding citizens.