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IntroductionThe discourse on immigration and immigrants is central to contemporary political and public discussions. Analyzing online conversations about immigrants provides valuable insights into public opinion, complemented by data from questionnaires on how attitudes are formed.MethodsThe research includes two studies examining the expressive and informational use of social media. Study 1 conducted a computational text analysis of comments on Singaporean Facebook pages and forums, focusing on how social media is used to discuss immigrants. Study 2 utilized survey data to examine the use of social media at the individual level, testing the relationships between cognitive ability, perceptions of threat, negative emotions towards immigrants, and social media usage within the Integrated Threat Theory framework.ResultsStudy 1 found that discussions about immigrants on social media often involved negative emotions and concerns about economic impact, such as competition for jobs and crime. Complementing these findings about perceived economic threats, Study 2 showed that individuals with higher social media usage and greater perceptions of threat were more likely to have negative emotions towards immigrants. These relationships were mediated by perceptions of threat and were stronger in individuals with lower cognitive abilities.DiscussionThe findings from both studies demonstrate the role of social media in shaping public attitudes towards immigrants, highlighting how perceived threats influence these attitudes. This research suggests the importance of considering how digital platforms contribute to public opinion on immigration, with implications for understanding the dynamics of attitude formation in the digital age.

Within organizational settings, communication dynamics are influenced by various factors, such as email content, historical interactions, and interpersonal relationships. We introduce the Email MultiModal Architecture (EMMA) to model these dynamics and predict future communication behavior. EMMA uses data related to an email sender’s social network, performance metrics, and peer endorsements to predict the probability of receiving an email response. Our primary analysis is based on a dataset of 0.6 million corporate emails from 4320 employees between 2012 and 2014. By integrating features that capture a sender’s organizational influence and likability within a multimodal structure, EMMA offers improved performance over models that rely solely on linguistic attributes. Our findings indicate that EMMA enhances email reply prediction accuracy by up to 12.5% compared to leading text-centric models. EMMA also demonstrates high accuracy on other email datasets, reinforcing its utility and generalizability in diverse contexts. Our findings recommend the need for multimodal approaches to better model communication patterns within organizations and teams and to better understand how relationships and histories shape communication trajectories.

Abstract Spatial aggregates of survey and web search data make it possible to identify the heterogeneous well-being effects of social media platforms. This study reports evidence from different sources of longitudinal data that suggests that the well-being effects of social media differ across platforms and population groups. The well-being effects of frequent social media visits are consistently positive for Facebook but negative for Instagram. Group-level analyses suggest that the positive well-being effects are experienced mainly by white, high-income populations at both the individual and the county level, while the adverse effects of Instagram use are observed on younger and Black populations. The findings are corroborated when geocoded web search data from Google is used and when self-reports from surveys are used in place of region-level aggregates. Greater Instagram use in regions is also linked to higher depression diagnoses across most sociodemographic groups.

In recent years, many studies have used social media data to make estimates of electoral outcomes and public opinion. This paper reports the findings from a meta-analysis examining the predictive power of social media data by focusing on various sources of data and different methods of prediction; i.e., (1) sentiment analysis, and (2) analysis of structural features. Our results, based on the data from 74 published studies, show significant variance in the accuracy of predictions, which were on average behind the established benchmarks in traditional survey research. In terms of the approaches used, the study shows that machine learning-based estimates are generally superior to those derived from pre-existing lexica, and that a combination of structural features and sentiment analyses provides the most accurate predictions. Furthermore, our study shows some differences in the predictive power of social media data across different levels of political democracy and different electoral systems. We also note that since the accuracy of election and public opinion forecasts varies depending on which statistical estimates are used, the scientific community should aim to adopt a more standardized approach to analyzing and reporting social media data-derived predictions in the future.

This study offers a comprehensive approach towards analyzing and explaining the role of Twitter in shaping and facilitating social movements especially during protests. It presents automatic and manual analyses of the tweet themes, usage characteristics and major Twitter users during a public outcry against a gangrape incident in Delhi, the capital city of India. Our results identified Twitter as an important channel for the diffusion of ideas and news among a vast set of adopters in defiance of geographical boundaries. Results of the content analyses highlight the prominent use of social media resources in disseminating information on Twitter, and the remarkable role of Twitter users as citizen journalists during the days of the protest. Results of the social network analysis suggest that major role players on Twitter were the offline protest leaders.