Jivitesh Jain

With Twitter growing as a preferred channel for outreach among major politicians, there have been focused efforts on online communication, even in election campaigns in primarily rural regions. In this paper, we examine the relationship between politicians’ use of social media and the level of urbanization and literacy by compiling a comprehensive list of Twitter handles of political party functionaries and election candidates in the run-up to the 2022 State Assembly elections in Uttar Pradesh, India. We find statistically significant relationships between political Twitter presence and levels of urbanization and with levels of literacy. We also find a strong correlation between vote share and Twitter presence in the winning party, a relationship that is even stronger in urban districts. This provides empirical evidence that social media is already a central part of electoral outreach processes in the Global South, but that this is still selectively more relevant to voters in, and politicians standing for elections from urban and higher-educated regions.

Progress in 3D object understanding has relied on manually “canonicalized” shape datasets that contain instances with consistent position and orientation (3D pose). This has made it hard to generalize these methods to in-the-wild shapes, e.g., from internet model collections or depth sensors. ConDor is a self-supervised method that learns to Canonicalize the 3D orientation and position for full and partial 3D point clouds. We build on top of Tensor Field Networks (TFNs), a class of permutation- and rotation-equivariant, and translation-invariant 3D networks. During inference, our method takes an unseen full or partial 3D point cloud at an arbitrary pose and outputs an equivariant canonical pose. During training, this network uses self-supervision losses to learn the canonical pose from an un-canonicalized collection of full and partial 3D point clouds. ConDor can also learn to consistently co-segment object parts without any supervision. Extensive quantitative results on four new metrics show that our approach out-performs existing methods while enabling new applications such as operation on depth images and annotation transfer.

On 6 January 2021, a mob of right-wing conservatives stormed the USA Capitol Hill interrupting the session of congress certifying 2020 Presidential election results. Immediately after the start of the event, posts related to the riots started to trend on social media. A social media platform which stood out was a free speech endorsing social media platform Parler; it is being claimed as the platform on which the riots were planned and talked about. Our report presents a contrast between the trending content on Parler and Twitter around the time of riots. We collected data from both platforms based on the trending hashtags and draw comparisons based on what are the topics being talked about, who are the people active on the platforms and how organic is the content generated on the two platforms. While the content trending on Twitter had strong resentments towards the event and called for action against rioters and inciters, Parler content had a strong conservative narrative echoing the ideas of voter fraud similar to the attacking mob. We also find a disproportionately high manipulation of traffic on Parler when compared to Twitter.

The extensive rise in consumption of online social media (OSMs) by a large number of people poses a
critical problem of curbing the spread of hateful content on these platforms. With the growing usage
of OSMs in multiple languages, the task of detecting and characterizing hate becomes more complex.
The subtle variations of code-mixed texts along with switching scripts only add to the complexity. This
paper presents a solution for the HASOC 2021 Multilingual Twitter Hate-Speech Detection challenge
by team Precog IIIT Hyderabad. We adopt a multilingual transformer based approach and describe our
architecture for all 6 subtasks as part of the challenge. Out of the 6 teams that participated in all the
subtasks, our submissions rank 3rd overall.

[Best Student Paper Award]
Social media has grown exponentially in a short period, coming to the forefront of communications and online interactions. Despite their rapid growth, social media platforms have been unable to scale to different languages globally and remain inaccessible to many. In this paper, we characterize Koo, a multilingual micro-blogging site that rose in popularity in 2021, as an Indian alternative to Twitter. We collected a dataset of 4.07 million users, 163.12 million follower-following relationships, and their content and activity across 12 languages. We study the user demographic along the lines of language, location, gender, and profession. The prominent presence of Indian languages in the discourse on Koo indicates the platform’s success in promoting regional languages. We observe Koo’s follower-following network to be much denser than Twitter’s, comprising of closely-knit linguistic communities. An N-gram analysis of posts on Koo shows a #KooVsTwitter rhetoric, revealing the debate comparing the two platforms. Our characterization highlights the dynamics of the multilingual social network and its diverse Indian user base.