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Politicians have used the web and social media to circumvent the gatekeeping behavior of traditional mass media by directly communicating with supporters in their accounts. This paper is aimed at understanding communication strategies used by politicians and campaigns, focusing on the role of gender cues in their visual self-presentation and their impact on election outcomes. Previous research has discussed the importance of visual portrayals of leaders in campaigns. These studies, however, have been mainly based on manual coding and are limited in scale and scope. This paper aims to fill the research gap by introducing a multi task method that infers perceived gender-stereotypical visual traits from social media images. We analyze 77,861 photographs collected from the Facebook accounts of 554 US politicians who ran in the 2018 elections. Regression analyses discover the positive association of the masculinity trait for electoral outcomes. We also identify an empirical evidence that the effect of gender stereotypes could vary according to the gender and party combinations of the candidates in a race. In the intersectional analysis, we found that the win of female democrats against the same gendered opponent was positively correlated with the femininity trait score. This study provides methodological foundations and empirical contributions to the understanding of politicians’ campaign behaviors via photographs shared on social media and their relation to electoral success.

 

Understanding who blames or supports whom in news text is a critical research question in computational social science. Traditional methods and datasets for sentiment analysis are, however, not suitable for the domain of political text as they do not consider the direction of sentiments expressed between entities. In this paper, we propose a novel NLP task of identifying directed sentiment relationship between political entities from a given news document, which we call directed sentiment extraction. From a million-scale news corpus, we construct a dataset of news sentences where sentiment relations of political entities are manually annotated. We present a simple but effective approach for utilizing a pretrained transformer, which infers the target class by predicting multiple question-answering tasks and combining the outcomes. We demonstrate the utility of our proposed method for social science research questions by analyzing positive and negative opinions between political entities in two major events: 2016 U.S. presidential election and COVID-19. The newly proposed problem, data, and method will facilitate future studies on interdisciplinary NLP methods and applications. 

Government censorship—internet shutdowns, blockages, firewalls—impose significant barriers to the transnational flow of information despite the connective power of digital technologies. In this paper, we examine whether and how information flows across borders despite government censorship. We develop a semi-automated system that combines deep learning and human annotation to find co-occurring content across different social media platforms and languages. We use this system to detect co-occurring content between Twitter and Sina Weibo as Covid-19 spread globally, and we conduct in-depth investigations of co-occurring content to identify those that constitute an inflow of information from the global information ecosystem into China. We find that approximately one-fourth of content with relevance for China that gains widespread public attention on Twitter makes its way to Weibo. Unsurprisingly, Chinese state-controlled media and commercialized domestic media play a dominant role in facilitating these inflows of information. However, we find that Weibo users without traditional media or government affiliations are also an important mechanism for transmitting information into China. These results imply that while censorship combined with media control provide substantial leeway for the government to set the agenda, social media provides opportunities for non-institutional actors to influence the information environment. Methodologically, the system we develop offers a new approach for the quantitative analysis of cross-platform and cross-lingual communication.