3 research outputs found
Characterising User Content on a Multi-lingual Social Network
Social media has been on the vanguard of political information diffusion in
the 21st century. Most studies that look into disinformation, political
influence and fake-news focus on mainstream social media platforms. This has
inevitably made English an important factor in our current understanding of
political activity on social media. As a result, there has only been a limited
number of studies into a large portion of the world, including the largest,
multilingual and multi-cultural democracy: India. In this paper we present our
characterisation of a multilingual social network in India called ShareChat. We
collect an exhaustive dataset across 72 weeks before and during the Indian
general elections of 2019, across 14 languages. We investigate the cross
lingual dynamics by clustering visually similar images together, and exploring
how they move across language barriers. We find that Telugu, Malayalam, Tamil
and Kannada languages tend to be dominant in soliciting political images (often
referred to as memes), and posts from Hindi have the largest cross-lingual
diffusion across ShareChat (as well as images containing text in English). In
the case of images containing text that cross language barriers, we see that
language translation is used to widen the accessibility. That said, we find
cases where the same image is associated with very different text (and
therefore meanings). This initial characterisation paves the way for more
advanced pipelines to understand the dynamics of fake and political content in
a multi-lingual and non-textual setting.Comment: Accepted at ICWSM 2020, please cite the ICWSM versio
Under the Spotlight: Web Tracking in Indian Partisan News Websites
India is experiencing intense political partisanship and sectarian divisions.
The paper performs, to the best of our knowledge, the first comprehensive
analysis on the Indian online news media with respect to tracking and
partisanship. We build a dataset of 103 online, mostly mainstream news
websites. With the help of two experts, alongside data from the Media Ownership
Monitor of the Reporters without Borders, we label these websites according to
their partisanship (Left, Right, or Centre). We study and compare user tracking
on these sites with different metrics: numbers of cookies, cookie
synchronizations, device fingerprinting, and invisible pixel-based tracking. We
find that Left and Centre websites serve more cookies than Right-leaning
websites. However, through cookie synchronization, more user IDs are
synchronized in Left websites than Right or Centre. Canvas fingerprinting is
used similarly by Left and Right, and less by Centre. Invisible pixel-based
tracking is 50% more intense in Centre-leaning websites than Right, and 25%
more than Left. Desktop versions of news websites deliver more cookies than
their mobile counterparts. A handful of third-parties are tracking users in
most websites in this study. This paper, by demonstrating intense web tracking,
has implications for research on overall privacy of users visiting partisan
news websites in India