Israel's Strategic Ontology of Victimhood in News Media Coverage of the 2024 Maccabi Tel Aviv Football Violence in Amsterdam

Dataset Description:This dataset was created for the study Israel’s Strategic Ontology of Victimhood in News Media Coverage of the 2024 Maccabi Tel Aviv Football Violence in Amsterdam. It relates to how Western European and North American English-language media framed the violence surrounding the November 7, 2024, football match between Ajax Amsterdam and Maccabi Tel Aviv. Articles were retrieved from the Nexis Uni and ProQuest databases using the keywords Israel and Amsterdam for the period November 6–20, 2024. The geographical scope included Europe and North America. A total of 464 articles from approximately 142 unique news sources were collected, though some sources (e.g., BBC, CNN, Wall Street Journal) were heavily overrepresented. The data collection represents a census of indexed English-language news coverage within the search parameters. The dataset contains two major components: Framing Analysis: Each article was coded for its primary frame (Israeli Victimhood, Non-Israeli Victimhood, or Mutual Aggression) and up to five thematic sub-frames based on a developed dictionary. The coding prioritized the first 10 words of the article's headline and lead paragraph. Source Analysis: The first ten sources cited per article were documented along with their institutional affiliation and the stance taken (whether the source emphasized Israeli victimhood or acknowledged alternative perspectives). AI-assisted qualitative coding was conducted using OpenAI’s GPT-4 model, supplemented with manual intercoder reliability testing (Cohen’s Kappa ≈ 0.7). Key Metadata: Timeframe Covered: November 6–20, 2024 Source Databases: Nexis Uni, ProQuest Regions Covered: Europe, North America Number of Articles: 464 articles analyzed; 452 usable for final coding Number of News Sources: Approximately 142 Dominant Outlets: BBC, CNN, Wall Street Journal, New York Times, Daily Mail, Guardian, GB News, Canadian Press, AFP, DPA Primary Frames: Israeli Victimhood (IV), Non-Israeli Victimhood (NI), Mutual Aggression (MA) Sub-frames: 33 distinct sub-frames identified Intercoder Reliability: 90% overlap on sample manual coding (Cohen's Kappa ~ 0.7) Tools Used: OpenAI GPT-4 for content coding; manual verification Use and Access:This dataset is intended for researchers examining media framing, strategic ontologies, international conflict reporting, and political communication. It can support studies in journalism studies, media sociology, Middle East politics, conflict studies, and AI-assisted content analysis.