The characterization and detection of bots with their presumed ability to manipulate society on social media platforms have been subject to many research endeavors over the last decade. In the absence of ground truth data (i.e., accounts that are labeled as bots by experts or self-declare their automated nature), researchers interested in the characterization and detection of bots may want to tap into the wisdom of the crowd. But how many people need to accuse another user as a bot before we can assume that the account is most likely automated? And more importantly, are bot accusations on social media at all a valid signal for the detection of bots? Our research presents the first large-scale study of bot accusations on Twitter and shows how the term bot became an instrument of dehumanization in social media conversations since it is predominantly used to deny the humanness of conversation partners. Consequently, bot accusations on social media should not be naively used as a signal to train or test bot detection models.

Recent advances in the field of generative artificial intelligence (AI) have blurred the lines between authentic and machine-generated content, making it almost impossible for humans to distinguish between such media. One notable consequence is the use of AI-generated images for fake profiles on social media. While several types of disinformation campaigns and similar incidents have been reported in the past, a systematic analysis has been lacking.

NLP models are used in a variety of critical social computing tasks, such as detecting sexist, racist, or otherwise hateful content.Therefore, it is imperative that these models are robust to spurious features.Past work has attempted to tackle such spurious features using training data augmentation, including Counterfactually Augmented Data (CADs).CADs introduce minimal changes to existing training data points and flip their labels; training on them may reduce model dependency on spurious features.However, manually generating CADs can be time-consuming and expensive.Hence in this work, we assess if this task can be automated using generative NLP models.We automatically generate CADs using Polyjuice, Chat-GPT, and Flan-T5, and evaluate their usefulness in improving model robustness compared to manually-generated CADs.By testing both model performance on multiple out-of-domain test sets and individual data point efficacy, our results show that while manual CADs are still the most effective, CADs generated by Chat-GPT come a close second.One key reason for the lower performance of automated methods is that the changes they introduce are often insufficient to flip the original label. 1Warning: This paper has instances of hateful and sexist language to serve as examples.

At the end of October 2022, Elon Musk concluded his acquisition of Twitter. In the weeks and months before that, several questions were publicly discussed that were not only of interest to the platform's future buyers, but also of high relevance to the Computational Social Science research community. For example, how many active users does the platform have? What percentage of accounts on the site are bots? And, what are the dominating topics and sub-topical spheres on the platform? In a globally coordinated effort of 80 scholars to shed light on these questions, and to offer a dataset that will equip other researchers to do the same, we have collected all 375 million tweets published within a 24-hour time period starting on September 21, 2022. To the best of our knowledge, this is the first complete 24-hour Twitter dataset that is available for the research community. With it, the present work aims to accomplish two goals. First, we seek to answer the aforementioned questions and provide descriptive metrics about Twitter that can serve as references for other researchers. Second, we create a baseline dataset for future research that can be used to study the potential impact of the platform's ownership change.

Social media can be a mirror of human interaction, society, and historic disruptions. Their reach enables the global dissemination of information in the shortest possible time and, thus, the individual participation of people worldwide in global events in almost real-time. However, these platforms can be equally efficiently used in information warfare to manipulate human perception and opinion formation. Within this paper, we describe a dataset of raw tweets collected via the Twitter Streaming API in the context of the onset of the war, which Russia started in Ukraine on February 24, 2022. A distinctive feature of the dataset is that it covers the period from one week before to one week after Russia invasion of Ukraine. This paper details the acquisition process and provides first insights into the content of the data stream. In addition, the data has been annotated with availability tags, resulting from rehydration attempts at two points in time: directly after data acquisition and shortly before manuscript submission. This may provide information on Twitter moderation policies. Further, we provide a detailed list of other published dataset covering the same topic. On the content level, we can show that our dataset comprises several distinct topics related to the conflict and conspiracy narratives -- topics that deserve more profound investigation. Therefore, the presented dataset is also made available to the community in an extended version with pseudonymized tweet content upon request.