With the advancement of digital technologies and gadgets, online content has become easily accessible. At the same time, harmful content also spread widely. There are different harmful content types present on various platforms in multiple languages. The topic of harmful content is broad and covers multiple research directions. Users of platforms are affected by all of them. In research, the different forms are mostly analysed separately, e.g. misinformation, cyber-bullying and hate speech. Most research has been conducted for only one platform, for a monolingual situation or on a particular issue. Counter-measures like blocking are down-ranking can make harmful content spreaders to switch platforms and languages to continuously reach a user base. Harmful content does not only appear on social media but also on news media. Spreader share harmful content in posts, news articles, comments and hyperlinks. There is a great need to study harmful content across platforms, languages, and topics. We plan to bring the research on harmful content under one umbrella such that different approaches and novel methods can be shared. The workshop will also cover the currently ongoing issues of war and elections. We propose the workshop, DHOW: Diffusion of Harmful Content on Online Web, which brings together the research on different topics of harmful content. We expect to discuss innovative research work and future research directions. The proposed workshop is the next iteration of DHOW 2024. https://dhow-workshop.github.io previously organized at ACM WebSci 2024 in Stuttgart, Germany.

Kein CA

False information could be dangerous if the claim is not debunked timely. Fact-checking organisations get a high volume of claims on different topics with immense velocity. The efficiency of the fact-checkers decreases due to 3V problems volume, velocity and variety. Especially during crises or elections, fact-checkers cannot handle user requests to verify the claim. Until now, no real-time curable centralised corpus of fact-checked articles is available. Also, the same claim is fact-checked by multiple fact-checking organisations with or without judgement. To fill this gap, we introduce FakeKG: A Knowledge Graph-Based approach for improving Automated Fact-checking. FakeKG is a centralised knowledge graph containing fact-checked articles from different sources that can be queried using the SPARQL endpoint. The proposed FakeKG can prescreen claim requests and filter them if the claim is already fact-checked and provide a judgement to the claim. It will also categorise the claim's domain so that the fact-checker can prioritise checking the incoming claims into different groups like health and election. This study proposes an approach for creating FakeKG and its future application for mitigating misinformation.