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

A systematic literature review on Wikidata

To review the current status of research on Wikidata and, in particular, of articles that either describe applications of Wikidata or provide empirical evidence, in order to uncover the topics of interest, the fields that are benefiting from its applications and which researchers and institutions are leading the work

Is there a Correlation Between Wikidata Revisions and Trending Hashtags on Twitter?

Twitter is a microblogging application used by its members to interact and stay socially connected by sharing instant messages called tweets that are up to 280 characters long. Within these tweets, users can add hashtags to relate the message to a topic that is shared among users. Wikidata is a central knowledge base of information relying on its members and machines bots to keeping its content up to date. The data is stored in a highly structured format with the added SPARQL protocol and RDF Query Language (SPARQL) endpoint to allow users to query its knowledge base

Wikidata for Scholarly Communication Librarianship

This is a static version of the text, provided here for archival purposes. For the best reading experience and for any recent updates, please use the Pressbooks version at: [LINK]https://iu.pressbooks.pub/wikidatascholcomm[/LINK]Wikidata for Scholarly Communication Librarianship was developed for anyone working in an academic library (or interested in working in an academic library) who may have a small or large role in supporting scholarly communication related services. The first two chapters, however, could serve as a basic introduction to Wikidata for anyone in academic librarianship. The remaining three chapters focus on a few topics that may be of more interest to those who work on open metadata, research metrics, and researcher profile projects.Scholarly Communication Notebook of the OER + ScholComm project (Josh Bolick, Maria Bonn, and Will Cross)

Analysis of Editors' languages in Wikidata

The Smart Stadium for Smarter Living project provides an end-to-end testbed for IoT innovation through a collaboration between Croke Park Stadium in Dublin, Ireland and Dublin City University, Intel and Microsoft. This enables practical evaluations of IoT solutions in a controlled environment that is small enough to conduct trials but large enough to prove and challenge the technologies. An evaluation of sound monitoring capabilities during the 2016 sporting finals was used to test the capture, transfer, storage and analysis of decibel level sound monitoring. The purpose of the evaluation was to use existing sound level microphones to measure crowd response to pre-determined events for display on big screens at half-time and to test the end-to-end performance of the testbed. While this is not the specific original purpose of the sound level microphones, it provided a useful test case and produced engaging content for the project. Analysis of the data streams showed significant packet loss during the events and further investigations were conducted to understand where and how this loss occurred. This paper describes the smart stadium testbed configuration using Intel gateways linking with the Azure cloud platform and analyses the performance of the system during the sound monitoring evaluation