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Social media has been on the vanguard of political information diffusion inthe 21st century. Most studies that look into disinformation, politicalinfluence and fake-news focus on mainstream social media platforms. This hasinevitably made English an important factor in our current understanding ofpolitical activity on social media. As a result, there has only been a limitednumber of studies into a large portion of the world, including the largest,multilingual and multi-cultural democracy: India. In this paper we present ourcharacterisation of a multilingual social network in India called ShareChat. Wecollect an exhaustive dataset across 72 weeks before and during the Indiangeneral elections of 2019, across 14 languages. We investigate the crosslingual dynamics by clustering visually similar images together, and exploringhow they move across language barriers. We find that Telugu, Malayalam, Tamiland Kannada languages tend to be dominant in soliciting political images (oftenreferred to as memes), and posts from Hindi have the largest cross-lingualdiffusion across ShareChat (as well as images containing text in English). Inthe case of images containing text that cross language barriers, we see thatlanguage translation is used to widen the accessibility. That said, we findcases where the same image is associated with very different text (andtherefore meanings). This initial characterisation paves the way for moreadvanced pipelines to understand the dynamics of fake and political content ina multi-lingual and non-textual setting.

Characterising User Content on a Multi-lingual Social Network