Misinformation and Its ImpactsHate Speech and Cyberbullying DetectionPsychology of Moral and Emotional Judgment
Abstract
As generative AI makes it easier to create synthetic visuals, AI-driven visual disinformation isbecoming more common on social media. However, while much research highlights its potentialharm, less is known about how to reduce its potential to mislead. In this study, we thereforeconducted a preregistered online experiment in the Netherlands (N=1,018) to test the effectivenessof various platform interventions: (1) AI labels or “watermarks,” (2) fact-check labels, and (3)community notes. We tested how effective these sources are in lowering credibility of the falsevisual and belief in the false claim it portrays across two polarizing topics: climate change andimmigration. Overall, the interventions showed no significant differences in effectiveness. Thiswas the case when pooling both topics together and for climate-change related disinformation inisolation. However, for visual disinformation about immigration, community notes were mosteffective, especially among participants with strong anti-migrant views. Our findings suggest thatwhile labeling has limited impact overall, its effectiveness varies by context, and no one-size-fits-all solution exists for combating AI-generated visual disinformation.
The adoption of generative artificial intelligence among communication practitioners and researchers surged after the launch of ChatGPT in November 2022, urging practitioners to critically engage in exploring pathways for fostering socially responsible and environmentally sustainable AI practices.
This R script (make_kessan10_csv.R) converts the Local Government Financial Settlement Survey (市町村別決算状況調), published by the Ministry of Internal Affairs and Communications on its annual pages of local government financial status survey materials, into machine-readable CSV. The source workbooks are print-oriented Excel files with multi-row merged headers, issued as four separate files per fiscal year (overview and expenditure, for cities and for towns and villages). The script consolidates them into long-format panels carrying fiscal year and municipality type as columns, and also writes one file per fiscal year. The output of a run over ten fiscal years (FY2015–FY2024) is deposited alongside it: all 1,741 municipalities, with 33 overview indicators and 94 expenditure items classified by purpose, giving panels of 17,410 rows each. Every municipality and every year is checked for internal consistency: the components of each expenditure category sum to that category's total, and the sum of all categories matches the total expenditure reported in the overview table. All checks passed for all ten years. Amounts are in thousands of yen, as published; blank cells are left blank rather than filled with zero. The column structure of the source data does not change over the period covered. One definitional change affects the adjusted ratio of current expenditure to current revenue: for FY2020 and FY2021 the special bonds issued for deferred tax collection are removed from current general revenue as well. Four changes of municipality occurred: Tomiya and Nakagawa became cities in FY2016 and FY2018 respectively, each receiving a new municipality code; Sasayama was renamed Tamba-Sasayama in FY2019, and Aogashima was renamed in FY2018 in the written form of its name only, both keeping their codes. The code was written with generative AI: Claude (Anthropic) was used to write and revise it. The author has verified the output and takes responsibility for the content. Version 1.1 corrects the reading of the census population change column in the overview table, where a small negative rate written with the triangle sign used in Japanese official statistics was left blank instead of being read as a number. 56 cells across the ten years were affected; no other value changed.
Yasutoshi Moteki· Zenodo (CERN European Organi...· 1 citation
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
