Research

As social media usage reaches record highs, personalization algorithms risk radicalizing users by reinforcing existing beliefs. However, evidence on how algorithms and user behavior jointly shape harmful online engagement is limited. In this paper, I conduct an individually randomized experiment with 8 million users of a prominent TikTok-like platform in India, replacing the feed-ranking algorithm with random content delivery. I focus on hateful content targeting minority groups, given its prominence on Indian social media and establish a trade-off: random post recommendation lowers exposure to anti-minority ("toxic") content by 27%, but at a substantial cost to the platform as overall platform usage falls by 35%. Strikingly, treated users share a larger proportion of the toxic posts they view, mitigating the decline in the number of toxic posts shared from the platform. Users with a higher interest in toxic content at baseline drive this result as they seek out posts the algorithm does not show them. I rationalize these findings with a model of a revenue-driven algorithm that faces heterogeneous users choosing which posts to consume. Counterfactual simulations evaluate alternative interventions that target toxicity in the algorithm's recommendations. Finally, I collect survey evidence to trace users' behavior beyond the platform and show that the most affected users substitute away to other platforms. These results underscore the limits of piecemeal algorithmic regulation intended to moderate harmful content online.

The internet, once hailed as a global space for free expression, is increasingly being restricted through government-imposed shutdowns. Despite the internet’s critical role in facilitating business operations, providing access to social protection, and coordinating protest, little is known about when, where, and why these shutdowns occur. We leverage a novel, high-resolution dataset to systematically document the extent and targeting of shutdown in India, the country with the most frequent internet shutdowns. We present five main findings. First, internet shutdowns are common and impacted more than 44% of India’s population in 2018-2022. Second, while shutdowns occur often, they are highly targeted. An average shutdown in our sample period and states lasts only 2 days and affects 4 districts. Third, we find that shutdowns exacerbate inequalities as they are disproportionately concentrated in poorer areas and in areas with larger Muslim populations. Fourth, shutdowns are more likely to occur immediately following both peaceful protests and violent riots. Fifth, on days when there were large scale protests against the national government, shutdowns are substantially more widespread in states that are politically aligned with the national government.

This paper investigates how ethnic violence and subsequent residential segregation shape children's lives across social groups. Using variation in communal violence due to a Hindu nationalist campaign tour across India, I show that violence displaces Muslims to segregated neighbourhoods. I exploit exogenous differences in the planned and actual route of the campaign trail to show that communal violence is associated with an increase in residential segregation of communities threatened by violence. Surprisingly, I find that post-event, Muslim primary education levels are higher in cities that were more susceptible to violence. For cohorts enrolling after the riots, the probability of attaining primary education decreases by 2.3% every 100 kilometres away from the campaign route. I interrogate the role of neighborhood effects in driving primary education outcomes across social groups in India.

How does the identity of local political leaders change public and private expression of political opinions? Using data from a very popular Indian content generation platform, we analyze if social media users are more likely to engage with hateful content when the local leader belongs to a vulnerable minority group. We consider engagement activity of 19 million users with one million Hindi political posts around the village council elections of April, 2021 in Uttar Pradesh, India. This is done to test the existence of a backlash effect when a member of the minority group gains political power. We employ a regression discontinuity design to compare anti-minority hate speech in villages where a Muslim candidate won an election with villages where Muslim candidates lost by a small margin. We find no change in engagement with toxic content in villages where the elected leader was Muslim after the election result was announced. A potential explanation is that engagement with hate is driven by electoral competition and campaigning efforts, and not the announcement of results. We also find evidence that engagement on the platform is generated by national news trends, and platform algorithms that do not take into account user location while generating customized content recommendations. This can make highly localized political conditions less salient for political engagement on social media platforms. This paper provides new evidence on political factors, like local election campaigns and identity of local political leaders, that are expected to change norms of engagement with political and toxic content on social media.