Despite ongoing efforts, online hate speech remains a pervasive issue on social media, particularly affecting vulnerable groups such as LGBTQ communities. While there is extensive debate around how best to address this problem, counter speech is emerging as a promising solution. However, existing research has primarily focused on detecting hateful content, often overlooking broader aspects such as the specific topics of discrimination and the spread of countermeasures online. This study examines the prevalence of hate speech and counter speech in LGBTQ online spaces on TikTok, analysing day-to-day interactions to identify recurring themes and targets. Results reveal that hate speech is widespread: at least 3.5% of messages contain hateful content, spread by approximately 4% of users, and one in three videos attracts hate comments or replies, primarily targeting LGBTQ topics explicitly. Gender identity emerges as a major focus, with transgender and non-binary individuals being frequent targets. Although much hate engagement goes unanswered, when responses occur, they are often in the form of counter speech, especially when LGBTQ-related topics are targeted. These findings improve our understanding of the nature and extent of online hate speech against LGBTQ communities, confirm counter speech as an employed response, and provide a foundation for further research aimed at developing strategies to promote safer, more inclusive social media environments.

This paper investigates the behavior of Reddit users who relied on alternative mobile apps, such as Apollo and RiF, before and after their forced shutdown by Reddit on July 1, 2023. The announcement of the shutdown led many observers to predict significant negative consequences, such as mass migration away from the platform. Using data from January to November 2023, we analyze user engagement and migration rates for users of these alternative clients before and after the forced discontinuation of their apps. We find that 22% of alternative client users permanently left Reddit as a result, and 45% of the users who openly threatened to leave if the changes were enacted followed through with their threats. Overall, we find that the shutdown of third-party apps had no discernible impact on overall platform activity. While the preceding protests were severe, ultimately for most users the cost of switching to the official client was likely far less than the effort required to switch to an entirely different platform. Scientific attention has increased to understand the contributing factors and effects of migration between online platforms, but real-world examples with available data remain rare. Our study addresses this by examining a large-scale online migratory movement.

Because of its willingness to share data with academia and industry, Twitter has been the primary social media platform for scientific research as well as for consulting businesses and governments in the last decade. In recent years, a series of publications have studied and criticized Twitter's APIs and Twitter has partially adapted its existing data streams. The newest Twitter API for Academic Research allows to "access Twitter's real-time and historical public data with additional features and functionality that support collecting more precise, complete, and unbiased datasets. The main new feature of this API is the possibility of accessing the full archive of all historic Tweets. In this article, we will take a closer look at the Academic API and will try to answer two questions. First, are the datasets collected with the Academic API complete? Secondly, since Twitter's Academic API delivers historic Tweets as represented on Twitter at the time of data collection, we need to understand how much data is lost over time due to Tweet and account removal from the platform. Our work shows evidence that Twitter's Academic API can indeed create (almost) complete samples of Twitter data based on a wide variety of search terms. We also provide evidence that Twitter's data endpoint v2 delivers better samples than the previously used endpoint v1.1. Furthermore, collecting Tweets with the Academic API at the time of studying a phenomenon rather than creating local archives of stored Tweets, allows for a straightforward way of following Twitter's developer agreement. Finally, we will also discuss technical artifacts and implications of the Academic API. We hope that our work can add another layer of understanding of Twitter data collections leading to more reliable studies of human behavior via social media data.