The death of Indian film star Sushant Singh Rajput at the peak of the COVID lockdown triggered chaos on the news cycle in India with a range of conspiracy theories that led to a witch hunt of sorts, and the hounding of several entertainers and public figures in the months that followed. Using data from Twitter, YouTube, and an archive of debunked misinformation stories, we examine the drivers and consequences of social media outrage in this case. We analyse these patterns from the framework of conspiracy and astroturfing and contextualize our findings to the socio-political background currently prevalent in India. Primarily, retweet rates on Twitter suggest that commentators benefited from talking about the case, which got higher engagement than other topics. Moreover, we report evidence of political hands in the way the discourse has shaped online, but more importantly that the story bears warnings for the shape and impact of witch-hunts in the backdrop of a fractured media environment. In conclusion, we consider the effects of Rajput’s outsider status as a small-town implant in the film industry within the broader narrative of systemic injustice, as well as the gendered aspects of mob justice that have taken aim at his former partner in the months since.

Social Media is increasingly central to culture wars, and in recent years has been at the center of debates around the weaponization of peoples’ opinions in polarized situations. We examine one such issue, an attempt to allow schools and junior colleges to ban girls from wearing Hijab in India in the southern state of Karnataka, India, by studying Twitter messaging related to the issue in early 2022, when it was in the news. We find that the narrative supporting the ban of the Hijab on Twitter is primarily driven by a minority of highly-influential individuals who are predominantly male and polarised in favour of the ruling party, while the discourse against the ban relies largely on influencers from within the Muslim community. Our findings show that social media can be a useful tool to craft the contours of politically-motivated escalation in the Global South.

We present an in-depth, language-centric, large-scale study of topical discussions on Twitter by 1,711 Indian politicians from 20 competing political parties in a 4-year timespan. We first show that politicians of all parties collectively indulge more in establishing personal branding through low-substance, personality-focused messaging as opposed to broadcasting policy stances. Additionally, compared to the party-in-power, opposition politicians collectively post more complex tweets and demonstrate higher negativity (e.g., using person-based attack hashtags) especially regarding the issue of corruption. Finally, through a contextual examination of the most retweeted messages from two key leaders---the prime minister and the leader of the largest opposition party, we find that there are qualitatively important distinctions between their styles---while the former focuses on positive-themed messaging, the latter employs confrontation and aggressive language with direct attacks on individuals and issues.

In this mixed-methods study of political discourse, we study the affordances of Twitter in the context of free speech in India. We critically examine specific cases of the legal prosecution of free speech and the use of extreme speech in attacks on people to document the risks to citizens when they engage in antagonistic online discourse, particularly against the state or political institutions. We follow this up with quantitative study of the use of extreme speech through 477 hashtags used by a random sample of thousand political actors on Twitter and find that politicians are rewarded, through higher retweet rates, when they engage in extreme or uncivil messaging. We contextualize these findings to the postcolonial history of India and the laws and institutions that enable differential consequences for engaging in various forms of speech. In conclusion, we propose that the affordances of new ICTs like social media need to be carefully considered for their unintended consequences, and that functional access to free speech may differ dramatically based on one's access to institutions.

In the aftermath of the COVID-19 crisis, there has been a massive amount of misinformation both related to the condition, and a range of linked social and economic issues. We present a mixed methods study of misinformation debunked by Indian fact checking agencies since January 2020. Alongside this, we present an analysis of what politicians in India have been discussing in the overlapping period. We find that affective issues dominate misinformation, especially in the period following the lockdown in India. Furthermore, we find that communal prejudice emerges as a central part of the misinformation environment, something that is reflected in the political speech around the same period.

We studied the topical preferences of social media campaigns of India's two main political parties by examining the tweets of 7382 politicians during the key phase of campaigning between Jan - May of 2019 in the run up to the 2019 general election. First, we compare the use of self-promotion and opponent attack, and their respective success online by categorizing 1208 most commonly used hashtags accordingly into the two categories. Second, we classify the tweets applying a qualitative typology to hashtags on the subjects of nationalism, corruption, religion and development. We find that the ruling BJP tended to promote itself over attacking the opposition whereas the main challenger INC was more likely to attack than promote itself. Moreover, while the INC gets more retweets on average, the BJP dominates Twitter's trends by flooding the online space with large numbers of tweets. We consider the implications of our findings hold for political communication strategies in democracies across the world.

We present a compiler-based scheme to protect the confidentiality of sensitive data in low-level applications (e.g. those written in C) in the presence of an active adversary. In our scheme, the programmer marks sensitive data by lightweight annotations on the top-level definitions in the source code. The compiler then uses a combination of static dataflow analysis, runtime instrumentation, and a novel taint-aware form of control-flow integrity to prevent data leaks even in the presence of low-level attacks. To reduce runtime overheads, the compiler uses a novel memory layout.