Characterising User Content on a Multi-lingual Social Network

Social media has been on the vanguard of political infor- mation 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 understand- ing 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 char- acterisation 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 Tel- ugu, 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 there- fore 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

What makes papers visible on social media? An analysis of various document characteristics

In this study we have investigated the relationship between different document characteristics and the number of Mendeley readership counts, tweets, Facebook posts, mentions in blogs and mainstream media for 1.3 million papers published in journals covered by the Web of Science (WoS). It aims to demonstrate that how factors affecting various social media-based indicators differ from those influencing citations and which document types are more popular across different platforms. Our results highlight the heterogeneous nature of altmetrics, which encompasses different types of uses and user groups engaging with research on social media.Comment: Presented at the 21th International Conference in Science & Technology Indicators (STI), 13-16, September, 2016, Valencia, Spai

Signed Link Analysis in Social Media Networks

Numerous real-world relations can be represented by signed networks with positive links (e.g., trust) and negative links (e.g., distrust). Link analysis plays a crucial role in understanding the link formation and can advance various tasks in social network analysis such as link prediction. The majority of existing works on link analysis have focused on unsigned social networks. The existence of negative links determines that properties and principles of signed networks are substantially distinct from those of unsigned networks, thus we need dedicated efforts on link analysis in signed social networks. In this paper, following social theories in link analysis in unsigned networks, we adopt three social science theories, namely Emotional Information, Diffusion of Innovations and Individual Personality, to guide the task of link analysis in signed networks.Comment: In the 10th International AAAI Conference on Web and Social Media (ICWSM-16