Gautam Kishore Shahi

During major political events, social media platforms encounter increased systemic risks. However, it is still unclear if and how they adjust their moderation practices in response. The Digital Services Act Transparency Database provides—for the first time—an opportunity to systematically examine content moderation at scale, allowing researchers and policymakers to evaluate platforms’ compliance and effectiveness, especially at high-stakes times. Here we analyze 1.58 billion self-reported moderation actions by the eight largest social media platforms in Europe over an eight-month period surrounding the 2024 European Parliament elections. We found that platforms did not exhibit meaningful signs of adaptation in moderation strategies as their self-reported enforcement patterns did not change significantly around the elections. This raises questions about whether platforms made any concrete adjustments, or whether the structure of the database may have masked them. On top of that, we reveal that initial concerns regarding platforms’ transparency and accountability still persist one year after the launch of the Transparency Database. Our findings highlight the limits of current co-regulatory and self-regulatory approaches, and point to the need for stronger enforcement and better data access mechanisms to ensure that online platforms meet their responsibilities in protecting the democratic processes.

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 brought the research on harmful content under one umbrella such that different approaches and novel methods can be shared. The workshop covers 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.

Online toxic content has grown into a pervasive phenomenon, intensifying during times of crisis, elections, and social unrest. A significant amount of research has been focused on detecting or analyzing toxic content using machine-learning approaches. The proliferation of toxic content across digital platforms has spurred extensive research into automated detection mechanisms, primarily driven by advances in machine learning and natural language processing. Overall, the present study represents the synthesis of 140 publications on different types of toxic content on digital platforms. We present a comprehensive overview of the datasets used in previous studies focusing on definitions, data sources, challenges, and machine learning approaches employed in detecting online toxicity, such as hate speech, offensive language, and harmful discourse. The dataset encompasses content in 32 languages, covering topics such as elections, spontaneous events, and crises. We examine the possibility of using existing cross-platform data to improve the performance of classification models. We present the recommendations and guidelines for new research on online toxic consent and the use of content moderation for mitigation. Finally, we present some practical guidelines to mitigate toxic content from online platforms.