Social media filtering and democracy: Effects of social media news use and uncivil political discussions on social media unfriending

Narrative versus statistical messages: The interplay of perceived susceptibility and misperceptions on vaccine intention

Although there is enough scientific evidence to show the benefits and safety of vaccines, vaccine rates are low, while misperceptions about vaccines are on the rise. The main purposes of the current study are to 1) examine the effects of narrative vs. statistical messages on vaccine intention, 2) test the mediating role of perceived expectancies, and 3) examine the moderating roles of perceived susceptibility and misperceptions on vaccine intention. Data were collected with an online experiment through Amazon Mturk. The online experiment was conducted via Qualtrics once the study was considered exempt by the Institutional Research Board of a large University in the U.S. A total of 300 participants aged 18 and above completed the survey. Findings show that perceived expectancies mediate the relationship between message manipulation and vaccine intention. Our findings also show a three-way interaction which indicates that among individuals with high misperceptions, statistical messages are more persuasive for individuals with high perceived susceptibility, while narrative messages are more influential for individuals with low perceived susceptibility.

A systematic review of worldwide causal and correlational evidence on digital media and democracy

One of today's most controversial and consequential issues is whether the global uptake of digital media is causally related to a decline in democracy. We conducted a systematic review of causal and correlational evidence (N = 496 articles) on the link between digital media use and different political variables. Some associations, such as increasing political participation and information consumption, are likely to be beneficial for democracy and were often observed in autocracies and emerging democracies. Other associations, such as declining political trust, increasing populism and growing polarization, are likely to be detrimental to democracy and were more pronounced in established democracies. While the impact of digital media on political systems depends on the specific variable and system in question, several variables show clear directions of associations. The evidence calls for research efforts and vigilance by governments and civil societies to better understand, design and regulate the interplay of digital media and democracy.

Language-agnostic deep learning framework for automatic monitoring of population-level mental health from social networks

In many countries, mental health issues are among the most serious public health concerns. National mental health statistics are frequently collected from reported patient cases or government-sponsored surveys, which have restricted coverage, frequency, and timeliness. Many domains of study, including public healthcare and biomedical informatics, have recently adopted social media data as a feasible real-time alternative to traditional methods of gathering representative information at the population level in a variety of contexts. However, because of the limits of fundamental natural language processing tools and labeled corpora in countries with limited natural language resources, such as Thailand, implementing social media systems to monitor mental health signals could be challenging. This paper presents LAPoMM, a novel framework for monitoring real-time mental health indicators from social media data without using labeled datasets in low-resource languages. Specifically, we use cross-lingual methods to train language-agnostic models and validate our framework by examining cross-correlations between the aggregate predicted mental signals and real-world administrative data from Thailand's Department of Mental Health, which includes monthly depression patients and reported cases of suicidal attempts. A combination of a language-agnostic representation and a deep learning classification model outperforms all other cross-lingual techniques for recognizing various mental signals in Tweets, such as emotions, sentiments, and suicidal tendencies. The correlation analyses discover a strong positive relationship between actual depression cases and the predicted negative sentiment signals as well as suicide attempts and negative signals (e.g., fear, sadness, and disgust) and suicidal tendency. These findings establish the effectiveness of our proposed framework and its potential applications in monitoring population-level mental health using large-scale social media data. Furthermore, because the language-agnostic model utilized in the methodology is capable of supporting a wide range of languages, the proposed LAPoMM framework can be easily generalized for analogous applications in other countries with limited language resources.

Impact of Social Media News Overload on Social Media News Avoidance and Filtering: Moderating Effect of Media Literacy

In the present era of information technology, people tend to seek out news to enhance their current knowledge and awareness and to gain literacy. The reliance on seeking out news and relevant information has become very necessary to accomplish personal and organizational objectives. The present study has undertaken an inquiry to investigate the impact of social media news overload on news avoidance and news filtering with the mediating and moderating mechanisms of the need for news and media literacy, respectively. For this purpose, data were obtained from 358 Chinese social media users through the aid of survey forms. The data obtained were then analyzed through Smart-PLS software. The statistical technique used for analysis is structural equation modeling (SEM) to determine the validity of the proposed hypotheses. The results of the study indicated that social media news overload has a significant effect on news avoidance, the need for news, and news filtering behavior. It was also observed that the need for news had a significant impact on news avoidance. In addition to this, it was also revealed that the need for news significantly mediated the relationship between social media news overload and news avoidance; however, it did not mediate the relationship between social media news overload and news filtering. Lastly, it was identified that media literacy significantly moderated the relationship between the need for news and news avoidance and it did not moderate the relationship between the need for news and news filtering behavior. This study has made important theoretical contributions by advancing the current literature in terms of the empirical evidence that indicates a significant relationship between social media news overload, news avoidance, and news filtering. Practically, this study contributed by emphasizing the need to encourage and train people to use strategies to seek relevant news in a vast repository of information available through information technology.

"I enjoy thinking critically, and I'm in control": Examining the influences of media literacy factors on misperceptions amidst the COVID-19 infodemic

Misinformation circulation has arguably reached a peak during the COVID-19 pandemic, creating an “infodemic” that severely endangers public health and well-being. Using a moderated mediation model, a survey of 712 respondents from China reveals that social media information seeking is positively associated with COVID-19 misperceptions, while need for cognition (NFC) is negatively associated with it. Both relationships became more significant while mediating through individuals' general misperceptions. Moreover, it is found that among those with greater locus of control over media, the association between social media information seeking and COVID-19 misperceptions became more positive, while the association between NFC and COVID-19 misperceptions became more negative among those with greater media locus of control. Findings provide insights into the misperception research and have practical implications regarding infodemic management.

Out-of-Place Content: How Repetitive, Offensive, and Opinion-Challenging Social Media Posts Shape Users’ Unfriending Strategies in Spain

Filtering strategies enable social media users to remove undesired content from their feeds, potentially creating homophilic environments. Although previous studies have addressed the individual-level factors and content features that influence these decisions, few have solely focused on users’ perceptions. Accordingly, this study applies social exchange theory to understand how users socially construct the process of unfriending. Based on 30 in-depth interviews with young Spaniards, we identify a widespread pattern of rejection over repetitive, opinion-challenging, and offensive posts, which we conceptualize as out-of-place content, a type of social media stimulus that hinders substantive online exchanges and challenges users’ understanding of social reality and individual values. This study contributes to current literature on unfriending by suggesting that filtering strategies are implemented gradually when posts overwhelm users’ tolerance threshold. Our findings also suggest that their deployment hinges on the closeness of the relationship between peers and social commitments formed in specific platforms. Future research is needed to assess to what extent the patterns identified in our interviews are present in the overall population.

The Impact of Pandemic Perception, National Feeling, and Media Use on the Evaluation of the Performance of Different Countries in Controlling COVID-19 by Chinese Residents

Different nations responded to the global spread of COVID-19 differently. How do people view the governance practices and effects of various countries? What factors affect their views? Starting from the three-dimensional model of cognitive-affective-media, this study examines how pandemic perception, the national feeling, which is the emotional preference of public for different countries, and media use affect the Chinese public views on the performance of other countries in controlling COVID-19. After performing regression analysis on the data of 619 Chinese public samples collected by an online survey, it reveals the following: (1) pandemic perception is negatively correlated with the evaluation of controlling-pandemic performance in different countries by Chinese residents, whereas national feeling is positively correlated with the evaluation of controlling-pandemic performance. (2) The use of media has different characteristics in the evaluation of controlling-pandemic performance in different countries by Chinese residents. Television has a significant influence on the evaluation of controlling-pandemic performance in the United States, China, and Germany by Chinese residents. (3) Collectivist cultural orientation has no significant impact on the evaluation of the anti-pandemic performance of different countries by Chinese residents, whereas virus perception only has a significant impact on the evaluation of the controlling-pandemic performance of the United States and Italy. Research has confirmed the existence of the cognitive-affective-media model in the evaluations by public on the governance of other countries, and prospects for the superimposed role of media in the cognitive-affective model.