Taberez Ahmed Neyazi

Does exposure to news about out-party campaigns’ increasing use of deepfakes further increase polarization among partisans, and how does this impact vary among different evaluations of political coalitions, parties, and leaders? What role does approval for candidates’ usage of AI play as a moderator in this relationship? This paper examines these questions using data from a survey experiment ( N = 1627), in which each respondent exposed to a news story that varied in their coverage of the growing deployment of AI-powered deepfakes during the 2024 India’s national election campaigns. The findings indicate that exposure to news about out-party campaigns’ use of deepfakes did not polarize attitudes towards coalitions, parties, and leaders. In contrast, approval for candidates’ use of AI was negatively associated with polarization towards coalitions, parties, and leaders, but this did not moderate the experimental effects: respondents who approved of candidates’ AI usage and saw a news article about out-party use of deepfakes were not more polarized toward either coalitions, parties or leaders. This study not only extends the understanding of AI’s role in campaigns by examining its complex effects on affective polarization, but it also broadens empirical research into the impact of deepfakes by focusing on the non-Western context of India, thereby offering new theoretical insights into how technological advancements intersect with political behaviors and attitudes.

In contemporary political discourse, the concern of affective polarization, often fueled by uncivil and sexist discourse, is notably evident in digital communication contexts like WhatsApp. In this study, we examined the potential effects of uncivil and sexist messages as well as moderator interventions against these messages coming from political in- and out-group members in such political groups through a survey experiment ( N = 1,464) fielded during the 2022 Uttar Pradesh (India) Legislative Assembly Elections. We investigate these questions and extend the prior work on affective polarization by introducing a new measure of Average Inter-party Affective Distance, which provides a single metric to index overall polarization in a multi-party election context. Our results showed that exposure to sexist uncivil messages had no discernible effect on affective polarization, political participation, and voting intention. Additionally, moderator warnings against incivility had no significant effects. However, we observed a variation in how respondents processed these messages based on their preexisting attitudes; notably, individuals with less sexist views were more inclined to vote after being exposed to sexist uncivil messages. These findings suggest that the influence of incivility and sexism in messaging platforms like WhatsApp on the wider electorate’s political behavior and attitudes is relatively limited. This research contributes to our understanding of the role of digital political communication, highlighting the importance of message content and preexisting individual differences in shaping affective polarization and mobilization.

Visual messaging is a cornerstone of campaign strategies of political parties and candidates that can complement and amplify the effects of the written/spoken word. Through a thematic analysis of the Facebook ads of the two main political parties during the 2021 West Bengal assembly elections in India, this paper shows the interplay of identity, ideology, and gender in the visual communication strategies of political parties on Facebook as they tried to mobilize voters in an intensely polarized context. Both the incumbent and the opposition parties framed issues in their visual campaigns that were culturally situated; these issues centered around identity and ideology while simultaneously emphasizing strong leadership with gendered rhetoric. Our findings contribute to the advancement of theoretical understanding of political personalization, highlighting the intricate interplay between gender, ideological inclinations, and cultural identity, all of which profoundly influence the personalization process in the context of an intensely polarized election campaign.

Public opinion on new technologies, like artificial intelligence (AI), is influenced by media coverage. However, it remains unclear as to what extent seeking news and information about AI on legacy media as opposed to social media can shape trust in AI. A cross-national survey conducted across Malaysia, Indonesia, Singapore and India investigated the impact of information seeking behaviour on trust in AI, as well as the moderating role of concern about misinformation online. Results indicate a positive relationship exists between seeking AI information on social media and trust across all countries. However, for traditional media, this association was only present in Singapore. When considering misinformation, a positive moderation effect was found for social media in Singapore and India, whereas a negative effect was observed for traditional media in Singapore. These findings have implications for the adoption of novel technologies and highlight the importance of understanding the role of media in shaping public trust.

This paper analyzes the representation of Indian Muslims in national news coverage during the COVID-19 pandemic. By deploying Critical Discourse Analysis (CDA), the paper examines how hegemonic news discourses in four Indian daily newspapers published in English and Hindi constructed the image of a Muslim “other” across various discursive moments in relation to the news coverage of a Tablighi Jamaat (TJ) congregation and its link to the spread of COVID-19 in India. Describing the congregation’s Muslim participants as anti-nationals, insensitive to the suffering of their compatriots, disrespectful of the law of the land, super-spreaders of the virus and jihadis, the image of the Muslim “other” was constructed and conveyed to the public by using such appellations. These conformed to many beliefs of Hindutva, the right-wing Hindu ideology, that seeks to infuse in public discourse feelings of the “other” – an other which needs to be criminalized and ostracized. The mediated reality produced is embedded in the power structure and is an ideologically driven exercise. The way hegemonic power operates is clearly reflected in the discourses about Muslims that circulated in the media during the first wave of COVID-19 in 2020.

Artificial Intelligence (AI) is becoming increasingly prevalent and its application highly sophisticated. Concerns about AI's vulnerabilities and future threats have, however, long been debated among scientists and the tech community. By integrating Beck's theory of risk society with an audience-centered sense-making approach, we seek to understand the effects of AI on the general public's daily lives and their concerns when they adopt AI technology. Five focus groups with 36 participants from Singapore, a technologically advanced country, were conducted to investigate their risk perceptions of AI and AI-powered technology. We found that participants were not passive consumers of content showing up on their news feeds; indeed, some participants attempted to outsmart the algorithms. They were aware that tech companies often tweak algorithms to personalize content and drive consumers into rabbit holes. Nevertheless, despite a certain level of awareness and sporadic attempts to “outsmart” the system, many users might still be influenced by these algorithms, underscoring the extent to which consumers are often manipulated by smart tech powered by AI. We discuss the theoretical and policy implications of our findings by looking at the contextual factors.

Does interactivity on Twitter initiated by political elites yield dividends in terms of indirect outcomes such as online political mobilization? Twitter has traditionally been utilized by political elites mainly as broadcasting media, characterized by its limited interactions with the wider public. In doing so, political elites may be overlooking Twitter's utility as an interactive media for mobilizing their supporters, which we seek evidence for in this paper. We adopt a naturalistic quasi-experimental design utilizing a unique dataset comprising tweets of users who were mentioned (36.62 million tweets) and not mentioned (29.73 million tweets) by Prime Minister Narendra Modi on 16th March, 2019 during his general election campaign. A controlled intervention analysis using Autoregressive Integrated Moving Average (ARIMA) models was carried out, which suggests that, on average, the total tweets count increased more for users mentioned by Modi (treatment group) as compared to those not mentioned by Modi (control group). However, no substantive lasting effect could be detected, indicating therefore that a mention by political elites helps generate immediate engagement which is not necessarily self-sustaining. To determine if this activation behavior is more prominent for less or more active users, we categorized the users based on their Twitter activity and assessed the significance of any movement between categories; no evidence could be found in support of this claim. Our findings thus provide important insights to understand how Twitter can be used as an effective platform for political campaigns by exploiting some of its interactive features such as replies and mentions to mobilize supporters.