Religion is a main aspect of life in many parts of the world; hence, it acts as a powerful tool for influencing people’s views and actions. In the context of propaganda and misinformation, religion has been perceived as a factor that impacts people negatively leading them to be influenced by false or biased information. However, there are limited quantitative studies that explore the exact role of religion in this process. In this study, we investigate whether state-sponsored propaganda accounts share religious content in a different way than the typical norms reported in the literature and whether they mobilize this content to promote their agendas. We find by exploring 15 Middle Eastern propaganda Twitter datasets encompassing around 124 million tweets from 32.5K accounts that propagandists share religious texts according to patterns that reflect aspects of their state’s agenda. We also demonstrate examples where such texts were used to modulate political messages.

There is a significant body of work looking at the ethical considerations of large language models (LLMs): critiquing tools to measure performance and harms; proposing toolkits to aid in ideation; discussing the risks to workers; considering legislation around privacy and security etc. As yet there is no work that integrates these resources into a single practical guide that focuses on LLMs; we attempt this ambitious goal. We introduce LLM Ethics Whitepaper, which we provide as an open and living resource for NLP practitioners, and those tasked with evaluating the ethical implications of others’ work. Our goal is to translate ethics literature into concrete recommendations for computer scientists. LLM Ethics Whitepaper distils a thorough literature review into clear Do’s and Don’ts, which we present also in this paper. We likewise identify useful toolkits to support ethical work. We refer the interested reader to the full LLM Ethics Whitepaper, which provides a succinct discussion of ethical considerations at each stage in a project lifecycle, as well as citations for the hundreds of papers from which we drew our recommendations. The present paper can be thought of as a pocket guide to conducting ethical research with LLMs.

Hate speech detection is key to online content moderation, but current models struggle to generalise beyond their training data.This has been linked to dataset biases and the use of sentence-level labels, which fail to teach models the underlying structure of hate speech.In this work, we show that even when models are trained with more fine-grained, spanlevel annotations (e.g., "artists" is labeled as target and "are parasites" as dehumanising comparison), they struggle to disentangle the meaning of these labels from the surrounding context.As a result, combinations of expressions that deviate from those seen during training remain particularly difficult for models to detect.We investigate whether training on a dataset where expressions occur with equal frequency across all contexts can improve generalisation.To this end, we create Unseen-PLEAD (U-PLEAD), a dataset of ∼364,000 synthetic posts, along with a novel compositional generalisation benchmark of ∼8,000 posts.Training on a combination of U-PLEAD and real data improves compositional generalisation while achieving state-of-the-art performance on the human-sourced PLEAD.

TikTok has seen exponential growth as a platform, fuelled by the success of its proprietary recommender algorithm which serves tailored content to every user - though not without controversy. Users complain of their content being unfairly suppressed by "the algorithm", particularly users with marginalised identities such as LGBTQ+ users. Together with content removal, this suppression acts to censor what is shared on the platform. Journalists have revealed biases in automatic censorship, as well as human moderation. We investigate experiences of censorship on TikTok, across users marginalised by their gender, LGBTQ+ identity, disability or ethnicity. We survey 627 UK-based TikTok users and find that marginalised users often feel they are subject to censorship for content that does not violate community guidelines. We highlight many avenues for future research into censorship on TikTok, with a focus on users' folk theories, which greatly shape their experiences of the platform.

Hadith, the recorded words and actions of the prophet Muhammad, is a key source of the instructions and foundations of Islam, alongside the Quran. Interpreting individual hadiths and verifying their authenticity can be difficult, even controversial, and the subject has attracted the attention of many scholars who have established an entire science of Hadith criticism. Recent quantitative studies of hadiths focus on developing systems for automatic classification, authentication, and information retrieval that operate over existing hadith compilations. Qualitative studies on the other hand try to discuss different social and political issues from the perspective of hadiths, or they inspect how hadiths are used in specific contexts in official communications and press releases for argumentation and propaganda. However, there are no studies that attempt to understand the actual presence of hadiths among Muslims in their daily lives and interactions. In this study, we try to fill this gap by exploring the presence of hadiths on Twitter from January 2019 to January 2023. We highlight the challenges that quantitative methods should consider while processing texts that include hadiths and we provide a methodology for Islamic scholars to validate their hypotheses about hadiths on big data that better represent the position of the society and Hadith influence

Sentiment analysis (SA) systems are widely deployed in many of the world's languages, and there is well-documented evidence of demographic bias in these systems.In languages beyond English, scarcer training data is often supplemented with transfer learning using pretrained models, including multilingual models trained on other languages.In some cases, even supervision data comes from other languages.Does cross-lingual transfer also import new biases?To answer this question, we use counterfactual evaluation to test whether gender or racial biases are imported when using cross-lingual transfer, compared to a monolingual transfer setting.Across five languages, we find that systems using cross-lingual transfer usually become more biased than their monolingual counterparts.We also find racial biases to be much more prevalent than gender biases.To spur further research on this topic, we release the sentiment models we used for this study, and the intermediate checkpoints throughout training, yielding 1,525 distinct models; we also release our evaluation code. 1

Abstract To proactively offer social media users a safe online experience, there is a need for systems that can detect harmful posts and promptly alert platform moderators. In order to guarantee the enforcement of a consistent policy, moderators are provided with detailed guidelines. In contrast, most state-of-the-art models learn what abuse is from labeled examples and as a result base their predictions on spurious cues, such as the presence of group identifiers, which can be unreliable. In this work we introduce the concept of policy-aware abuse detection, abandoning the unrealistic expectation that systems can reliably learn which phenomena constitute abuse from inspecting the data alone. We propose a machine-friendly representation of the policy that moderators wish to enforce, by breaking it down into a collection of intents and slots. We collect and annotate a dataset of 3,535 English posts with such slots, and show how architectures for intent classification and slot filling can be used for abuse detection, while providing a rationale for model decisions.1

Information systems such as social media strongly influence public opinion formation. Additionally, communication on the internet is shaped by individuals and organisations with various aims. This environment has given rise to phenomena such as manipulated content, fake news, and social bots. To examine the influence of manipulated opinions, we draw on the spiral of silence theory and complex adaptive systems. We translate empirical evidence of individual behaviour into an agent-based model and show that the model results in the emergence of a consensus on the collective level. In contrast to most previous approaches, this model explicitly represents interactions as a network. The most central actor in the network determines the final consensus 60–70% of the time. We then use the model to examine the influence of manipulative actors such as social bots on public opinion formation. The results indicate that, in a highly polarised setting, depending on their network position and the overall network density, bot participation by as little as 2–4% of a communication network can be sufficient to tip over the opinion climate in two out of three cases. These findings demonstrate a mechanism by which bots could shape the norms adopted by social media users.