Understanding Twitter as a political space, we observe how, in response to the actions of the elites, a society that had previously remained distant and in a process of radicalization comes together under the protection of hashtags and trending topics to engage in public debate. One of its tools of protest is memes, which are used as argumentative reinforcement or as an attack. Their repetitive use facilitates their viralization, expanding their criticism and thereby increasing their social impact. In this study, we will analyse a case in which memes played a significant role in the digital debate: The “Debate a cinco” or 4N Debate, broadcast on 4 November 2019, in which Albert Rivera — leader of the Ciudadanos party — presented before the cameras a visibly fragmented paving stone (panot) of considerable size. This action triggered an avalanche of comments and interactions on Twitter, which continued until the election day on 10 November. The public’s criticism and laughter generated a second media wave, in which digital news outlets, in their coverage of the event, compiled a collection of memes created at Rivera’s expense. This fuelled a social trend of irony and mockery directed at Rivera’s public persona, contributing to a drop from 57 to 10 parliamentary seats, the party’s relegation to fifth place and his subsequent resignation from the presidency of Ciudadanos and an end to his political career.

In the rapidly changing landscape of online social interactions, the presence of automated accounts, or bots, has always posed a significant challenge to maintaining platforms where the information posted is authentic and reliable. The emergence of large language models (LLMs) may exacerbate this problem, as researchers have recently found families of automated accounts that use generative artificial intelligence to produce their posts. This paper focuses on this new type of bot, particularly on detecting them. Using a new detection technique that relies on image classification to distinguish between human and automated accounts, we demonstrate remarkable efficiency in identifying bots whose posts are generated by large language models. Our research improves the results of previous work on the detection of bot accounts powered by generative artificial intelligence.

This study used semi-structured interviews (N = 23) with college students to explore hate-watching, defined here as “repeatedly watching people, things, or content we hate for the purpose of achieving some gratification.” Several important themes emerged from the analysis including (a) a pronounced desire for moral superiority and downward social comparison, (b) confirmation of existing beliefs, attitudes, values, and feelings, (c) a desire to understand “the other side” as perceived by hate-watchers, (d) a sense of “guilty pleasure”, and (e) social bonding. The findings offer new and exciting insight into modern viewing habits as they tend to defy the conventional wisdom of the literature regarding audiences gravitating toward content that affirms and reinforces existing beliefs, attitudes, values, and cognition. The implication is that hate-watching as a phenomenon may fuel societal polarization.

With the rapid development of artificial intelligence (AI), many tasks in the art field are transformed. A critical question emerges: how do artists perceive this change? This study investigates the perceptions of digital artists in the entertainment industry towards AI-generated art. Through 22 in-depth interviews, we find that artists considered AI art unethical. In addition, artists questioned the agency and the lack of human expression behind AI art. Furthermore, the creative process is a point of contention. Unlike traditional art forms, AI art offered them little control or room for experimentation. Artists could not iterate or make incremental changes. Regarding the final AI art output, although the digital artists were impressed with its aesthetics, they found AI art soulless. The study has important implications for emerging research on AI art.

Gender issues persist in the computing industry, with women representing only just over 20 percent of the workforce, despite numerous initiatives to encourage their wider participation. This paper explores gender inequalities within the computing industry by adapting the sociological lens of Pierre Bourdieu’s concepts of capital, habitus and field to understand why inequalities arise and persist in the computing industry. Using interviews with both men and women in the computing industry, the research explores the gender perceptions and systemic experiences from both perspectives. The findings indicate that while inequalities persist, women can and do demonstrate agency in creating their own social support networks to assist in negotiating the male dominated workplace. Promisingly, there is also an increasing awareness among men in the industry regarding the importance of inclusivity, coupled with the positive impact of such developments for them personally.

Youth online engagement and well-being are especially important in emerging countries, but they tend to be understudied. Rather, the existing research tends to be more on the United States. This study examined third place theory of virtual spaces on individual-level well-being among the Generation Z from Brazil and China, especially comparing them to their U.S. counterparts. Well-being, both related to realizing your full potential and lowering psychopathology, is relevant to youth engagement in online spaces, because the spaces can help the youth express their true selves and engage in supportive online communities. The participants were players of a mobile video game whose three months of pre-survey gameplay data and survey data (N = 986) were analyzed. We compared countries on several third place characteristics, on well-being, and how the third place characteristics predicted well-being. We found that players from the three countries were significantly different in several third place characteristics (i.e., neutral ground, leveler, and conversation). Well-being was also higher among the Chinese players. Moreover, the leveler characteristic–commonality of game levels among players and their in-game friends–significantly predicted well-being. These findings from a mobile game help better understand youth online perceptions and behaviors in different countries, correlates to well-being, and contribute to the theory development of virtual third places and digital public spheres.

Researchers rely on academic Web search engines to find scientific sources, but search engine mechanisms may selectively present content that aligns with biases embedded in queries. This study examines whether confirmation biased queries prompted into Google Scholar and Semantic Scholar will yield results aligned with a query’s bias. Six queries (topics across health and technology domains such as ‘vaccines’, ‘Internet use’) were analyzed for disparities in search results. We confirm that biased queries (targeting ‘benefits’ or ‘risks’) affect search results in line with bias, with technology-related queries displaying more significant disparities. Overall, Semantic Scholar exhibited fewer disparities than Google Scholar. Topics rated as more polarizing did not consistently show more disparate results. Academic search results that perpetuate confirmation bias have strong implications for both researchers and citizens searching for evidence. More research is needed to explore how scientific inquiry and academic search engines interact.

This study explores the experiences, usage patterns, and social comparison trends on Instagram among female college students, with a focus on body image. In response to alarming rates of body dissatisfaction among college women, particularly in comparison to a Meta Inc. study, which highlighted the disproportionate impact of social comparison on young females, this research delves into the firsthand accounts of young females regarding their past and current experiences, perceptions, and usage trends on the platform. Through interviews, recurring themes emerged, indicating that many female participants engaged in social comparison on Instagram, yielding both positive and negative outcomes. Participants reported adverse effects on mental and physical well-being due to comparisons on social media, aligning with existing literature. The study further revealed that participants intentionally adjusted their habits to mitigate the negative impact of social comparison, shedding light on the evolving usage, comparison, and perceptions of social media users in response to unwanted effects during young adulthood.

This paper uses the embodied, Jewish identities of its three authors, and the experimental methodology of kibbitzing as a form of collective inquiry and self-reflexive praxis in order to demonstrate the limitations of chatbots to produce humorous narratives from an explicitly Jewish epistemology. By contrasting the affordances of large language models (LLMs) and their associated chatbots with the context-based logics of Jewish joke craft and storytelling, this article goes on to demonstrate the risk of cultural erasure that is posed by the positivist, denotative meanings associated with ChatGPT’s attempts at producing jokes for, or about, Jews.