Social Science Computer Review

Britt, Brian C.

doi: 10.1177/08944393231220490pmid: N/A

The past several years have seen rising hate crimes, terrorist attacks, and broader extremist movements, with news reports often noting that these movements can be traced back to fringe online communities. Yet the question remains why such online groups appear more likely to foster radicalization than those in other contexts. This netnographic case study demonstrates how sexual appeals in fringe online communities facilitate the development of extremist ideologies. Specifically, the cognitive effects of sexual arousal combined with the social norms of such communities contribute to the acceptance of hate speech and fringe ideologies while reducing the extent to which audiences evaluate rational arguments and competing points of view. Thus, sexual appeals paired with messaging or imagery that promotes fringe points of view, which can be more freely expressed in small online groups than in other contexts, are more likely to result in intended attitudinal and behavioral changes—in other words, extremism.

Bouchillon, B. C.

doi: 10.1177/08944393231219192pmid: N/A

As racial and ethnic diversity have increased in America, prejudice too has expanded. Citizens are more wary of immigrants, with attitudes toward Asian immigrants in particular worsening during COVID-19. Yet less is known about the prejudice directed at other immigrant groups during this period, with research suggesting that feeling capable of interacting with new people could reduce misgivings about diversity. A web survey was conducted in April of 2020 to test the potential for digital and physical social competence to improve attitudes toward Mexican immigrants, as the largest immigrant group in the United States (N = 665). Interpersonal competence was inversely associated with prejudice toward Mexican immigrants, with interpersonal skills such as attentiveness, expressiveness, and mindfulness being especially valuable for prejudice reduction. Computer-mediated communication competence was indirectly associated with feeling less prejudiced, through interpersonal competence, and social presence also moderated the conversion of CMC competence into interpersonal competence, diminishing prejudice even further. Digital social capabilities encourage admiration and sympathy for immigrants by making users feel more capable of interacting with them locally. Networked settings now have the potential to train dissimilar users to interact together in person, as a way of reducing prejudice.

Wu, Yuehua; Cai, Weijia; Mensah, Sandra Asantewaa

doi: 10.1177/08944393231217940pmid: N/A

Despite the increasing popularity of female-oriented romantic video games (RVGs, also known as otome games) in Asia, research on this topic is scarce. Drawing upon social exchange theory and social cognitive theory, the current study examined the association of RVG involvement and desire for real-life romantic relationships (RLRRs), and tested a SEM path model delineating the possible pathways linking RVG involvement to RLRR desire. A survey method was adopted to collect data from female RVG players on an online otome games forum in China. Results from a valid sample of 353 respondents (aged 18 or older) showed the direct, indirect, and total effects of RVG involvement on players’ interest in real-life dating and marriage relationships were all negatively significant. It was found that gamers’ avatar identification and parasocial relationships with romantic targets significantly mediated the relationship between RVG involvement and RLRR desire. Adding to a comparatively under-explored line of inquiry on the role of computer games in shaping real-life romance, this study contributes to both game effects and romantic media consumption literature.

Liu, Piper Liping; Yeo, Tien Ee Dominic

doi: 10.1177/08944393231220487pmid: N/A

Despite the growing prevalence of social media usage among older adults, the impact for their well-being remains unclear. This study investigates the impact of social grooming on social media (SGSM) on the life satisfaction of a representative sample (N = 591) of older adults (aged 55 and above) in Taiwan. Using an indirect effects paradigm, the study examines the mediation mechanisms of bridging social capital and perceived social support in the relationship between SGSM and life satisfaction. Additionally, the moderating effect of social network size (SNS) is assessed. The results indicate that bridging social capital and social support fully and sequentially mediate the influence of SGSM on older adults’ life satisfaction. Furthermore, SNS is identified as a significant moderator in this sequential mediating effect. These findings contribute to the existing literature on social media use and highlight the importance of understanding the impact of SGSM on life satisfaction and other psychological outcomes for older adults. The results also emphasize the need to consider the unique characteristics and specific needs of older adults, and to promote and assist them in effectively using social media to expand their social networks and acquire social support, which are crucial for their life satisfaction.

Phillips, Justin Bonest

doi: 10.1177/08944393231224535pmid: N/A

In 2017, Facebook’s news feed algorithm began weighting emoji reactions (e.g., love and angry) as five times more valuable than the like button. Such a change is theoretically intriguing because existing research largely suggests that women tend to use emojis more than men on social media. Within the context of political campaigns, prior work has revealed a host of other “gender gaps,” from documenting men’s and women’s differing tolerance for negative campaigns, to examining variations in online political participation and—more broadly—charting gendered imbalances in party demographic support. To date, however, no study has looked to investigate this potential gender emoji gap within the online political environment. This paper explores just such a gap, combining data across three US election cycles (2016–2020), over thirty million individual observations, and thousands of (federal and state) candidates. The data shows that women exhibited a greater preference for emoji reactions than men in response to posts from the 2016 presidential election candidates. Party, and candidate negativity, also appeared to moderate this effect. Likely due to this (moderated) gender gap, Democratic candidates continued to see a much higher proportion of emoji reactions to their posts, than Republicans in 2018, and 2020. In turn, the results offer clear evidence of a persistent emoji gender gap in US political campaigns on Facebook. Such findings strengthen our theoretical understanding of political communication and behavior online, and prompt important questions going forward for future research.

Yang, Kun; Qian, Shuang

doi: 10.1177/08944393231219481pmid: N/A

This paper explores whether and in what situation the smiling face emoji will influence the interpretation of an utterance in a virtual context. The researchers drew examples from daily WeChat communication and posted them to participants in the experiment. Experimental studies found that the smiling face emoji decreases the politeness of an utterance but does not mitigate the illocutionary force of an impolite utterance. Further studies demonstrate that the interpretation is related to two features of WeChat: the interactant’s identity (age) and the situation of communication. For one thing, utterances with smiling face emoji may be interpreted as disrespectful by younger Chinese rather than the older. For another, the smiling face emoji is always interpreted as impolite when the utterances are related to the interactants’ feelings. We also infer from the findings that older people might respect the feelings of the addresser more than younger people in WeChat communication. This paper will help avoid miscommunication and contribute to understanding the socio-cultural features of interpersonal interaction in a virtual context.

Hu, Lingshu

doi: 10.1177/08944393231219685pmid: N/A

Political partisanship constitutes a pivotal group identity that significantly influences individuals’ voting behaviors and shapes their ideological and cultural perspectives. While traditional surveys and experimental studies can directly capture political identity by asking the participants, this task has become intricate when employing digital trace data sourced from social media. Previous classification methods, attempting to infer political identity from users’ networks or textual content, suffered from limited efficiency or generalizability. In response, this study introduces a two-step method that utilizes deep learning models to enhance classification efficiency, generalizability, and interpretability. In the first step, two deep learning models, trained on 2.5 million tweets from 825 Congressional politicians in the U.S., achieved accuracy rates of 87.71% and 89.54%, respectively, in detecting politicians’ partisanships based on their individual tweets. Subsequently, in the second step, by employing a simple machine learning model that leverages the aggregated predicted values derived from the first-step models, accuracy rates of 94.92% and 96.61% were attained for identifying non-politician users’ political identities based off their 50 and 200 tweets, respectively. In addition, an attention mechanism was integrated into the deep learning model to assess the contribution of each word in the classification process.

Konieczny, Piotr; Lewoniewski, Włodzimierz

doi: 10.1177/08944393231220165pmid: N/A

As one of the most popular sources of information in the world, Wikipedia is edited by a large, global community of contributors. User-generated nature of this online encyclopedia ensures that the information reflects a wide range of topics. Hovewer, Wikipedia articles are created and edited independently in each language version. Therefore, some topics may be presented with varying degrees of completeness depending on their importance in a particular language community. In this paper, we quantified the concept of Americanization on a global scale through comparative analysis of the coverage of American topics in different language versions of Wikipedia. For this purpose, we analyzed over 90 million Wikidata items and 40 million Wikipedia articles in 58 languages. We discussed whether Americanization is more or less dominant in different languages, regions, and cultures. We showed that the interest in American topics is not universal. Western, developed countries are more Americanized (more interested in topics related to America) than the rest of the world. This is the first global, quantitative confirmation of issues often hypothesized, or assumed, in the literature on Americanization and related phenomena. This study shows that Wikipedia and Wikidata can allow quantification of social science concepts that previously were considered not realistically measurable. Finally, the presented research is also relevant to the discourses on the biases of Wikipedia.

De Paoli, Stefano

doi: 10.1177/08944393231220483pmid: N/A

Large Language Models (LLMs) have emerged as powerful generative Artificial Intelligence solutions. This paper presents results and reflections of an experiment done with the LLM GPT 3.5-Turbo to perform an inductive Thematic Analysis (TA). Previous research has worked on conducting deductive analysis. Thematic Analysis is a qualitative method for analysis commonly used in social sciences and it is based on interpretations by the human analyst(s) and the identification of explicit and latent meanings in qualitative data. The paper presents the motivations for attempting this analysis; it reflects on how the six phases to a TA proposed by Braun and Clarke can partially be reproduced with the LLM and it reflects on what are the model’s outputs. The paper uses two datasets of open access semi-structured interviews, previously analysed by other researchers. The first dataset contains interviews with videogame players, and the second is a dataset of interviews with lecturers teaching data science in a University. This paper used the analyses previously conducted on these datasets to compare with the results produced by the LLM. The results show that the model can infer most of the main themes from previous research. This shows that using LLMs to perform an inductive TA is viable and offers a good degree of validity. The discussion offers some recommendations for working with LLMs in qualitative analysis.

Shin, Donghee; Jitkajornwanich, Kulsawasd

doi: 10.1177/08944393231225547pmid: N/A

Algorithmic radicalization is the idea that algorithms used by social media platforms push people down digital “rabbit holes” by framing personal online activity. Algorithms control what people see and when they see it and learn from their past activities. As such, people gradually and subconsciously adopt the ideas presented to them by the rabbit hole down which they have been pushed. In this study, TikTok’s role in fostering radicalized ideology is examined to offer a critical analysis of the state of radicalism and extremism on platforms. This study conducted an algorithm audit of the role of radicalizing information in social media by examining how TikTok’s algorithms are being used to radicalize, polarize, and spread extremism and societal instability. The results revealed that the pathways through which users access far-right content are manifold and that a large portion of the content can be ascribed to platform recommendations through radicalization pipelines. Algorithms are not simple tools that offer personalized services but rather contributors to radicalism, societal violence, and polarization. Such personalization processes have been instrumental in how artificial intelligence (AI) has been deployed, designed, and used to the detrimental outcomes that it has generated. Thus, the generation and adoption of extreme content on TikTok are, by and large, not only a reflection of user inputs and interactions with the platform but also the platform’s ability to slot users into specific categories and reinforce their ideas.