During the 2023–ongoing Gaza war, Palestinian advocacy on social media has faced rapid removals, downranking, and account sanctions. In this contribution, we offer a layered analysis of how people endure and counter this repression across affective, mechanistic, and material dimensions. Using patchwork ethnography over 295 first-person testimonies and 85 NGO/press documents, we identify a recursive Contest Loop: hostile mass-report brigades and automated enforcement that spur supporter “appeal brigades,” mirroring, and migration. Findings are organized as a three-layer ecology—Invisible Scars (whiplash, shadowbanning as probabilistic throttling, self-censorship), Dueling Brigades (frictions, coordinated reports, supporter procedures), and Feed-to-Street Ripples (fundraising, evidentiary preservation, livelihoods). Conceptually, we extend platform-assemblage thinking with a Resistance Assemblage: ad-hoc technical, emotional, and legal mutual-aid infrastructures that keep visibility alive under sanction. We contribute: (1) an event-centered, experience-near account of co-produced moderation in conflict; (2) two integrative lenses (Contest Loop, Resistance Assemblage); and (3) design/policy directions, including collective-appeal dashboards, and evidentiary safeguards that separate archiving from distribution.

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.

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

This paper proposes a simplified first-order nonlinear active disturbance rejection (SNADRC) controller for speed control of induction motor drives. The proposed technique eliminates the tracking differentiator component of the traditional NADR controller to cope with the complexity of the NADRC meanwhile leverage its advantages. The simulation results show the robustness of the proposed technique over the conventional proportional-Integral (PI)-based speed control against load disturbances. In addition, it show the superior dynamic performance achieved using the proposed technique compared to the first-order and second-order Linear ADR (LADR) controller.

The detection and analysis of fake news and its origins has become a main task associated with the overall objective of social media regulation in recent years. The majority of work has been dedicated towards detecting misinformation with some focus on analyzing the flow of fake news across social networks. However, there is less attention to the characteristics of social media users who consume this fake news. In this work, we investigate the possibility of predicting users' reactions towards fake news and defining some network characteristics for each users' group. We utilized a set of fact-checking websites in the Arab world that report social media posts spreading fake news and the interactions with them. We defined three sets of users: 1) Spreaders, who spread fake news, 2) Checkers, who constantly share fact-checking threads, and 3) Refuters, who respond to fake-news posts declaring their inaccuracy. We build a classifier that uses users' network graph to predict their reactions with an accuracy exceeding 93%. We applied further analysis for the most significant features of each users group and noticed that spreaders interact with more accounts that use their mother tongue, a considerable number of famous state-sponsored accounts, and accounts that get suspended while checkers and refuters interact with more foreign accounts and news-reporting entities. Central nodes in the networks of spreaders were found to be linked with state-sponsored media, whereas central nodes in the networks of checkers included organizations with a cross-cultural nature.

We present two comparable diachronic corpora of scientific English and German from the Late Modern Period (17th c.–19th c.) annotated with Universal Dependencies. We describe several steps of data pre-processing and evaluate the resulting parsing accuracy showing how our pre-processing steps significantly improve output quality. As a sanity check for the representativity of our data, we conduct a case study comparing previously gained insights on grammatical change in the scientific genre with our data. Our results reflect the often reported trend of English scientific discourse towards heavy noun phrases and a simplification of the sentence structure (Halliday, 1988; Halliday and Martin, 1993; Biber and Gray, 2011; Biber and Gray, 2016). We also show that this trend applies to German scientific discourse as well. The presented corpora are valuable resources suitable for the contrastive analysis of syntactic diachronic change in the scientific genre between 1650 and 1900. The presented pre-processing procedures and their evaluations are applicable to other languages and can be useful for a variety of Natural Language Processing tasks such as syntactic parsing.