@techreport{OswaldMunzert, type = {Working Paper}, author = {Oswald, Lisa and Munzert, Simon}, title = {Exposure to untrustworthy news media then and now: Declining news media quality over 7 years}, publisher = {OSF}, doi = {10.31235/osf.io/5ndjx_v1}, pages = {8}, abstract = {In a rapidly evolving digital media landscape, understanding how exposure to untrustworthy news changes over time is essential for evaluating its potential effects on public attitudes and behavior. However, there is limited evidence on how demand for untrustworthy news develops across longer time frames. Linking web data with surveys, we compare exposure to untrustworthy news sources across demographic and political groups and over a 7-year time frame for two samples of German adults (N = 1,212 in 2017 and N = 436 in 2024). Visits to untrustworthy news sources make up less than 1\% of media diets and are associated with low satisfaction with democracy and a preference for a far-right party. Propensity score matching reveals stability in untrustworthy news exposure, yet a notable decline in the average quality of news diets over 7 years. These results suggest that while untrustworthy news exposure remains limited and stable, a broader erosion in news quality may pose a growing challenge for informed democratic engagement.}, language = {en} } @techreport{HartmannOueslatiStauferetal., type = {Working Paper}, author = {Hartmann, David and Oueslati, Amin and Staufer, Dimitri and Pohlmann, Lena and Munzert, Simon and Heuer, Hendrik}, title = {Lost in Moderation: How Commercial Content Moderation APIs Over- and Under-Moderate Group-Targeted Hate Speech and Linguistic Variations}, publisher = {arXiv}, doi = {10.48550/arXiv.2503.01623}, pages = {27}, abstract = {Commercial content moderation APIs are marketed as scalable solutions to combat online hate speech. However, the reliance on these APIs risks both silencing legitimate speech, called over-moderation, and failing to protect online platforms from harmful speech, known as under-moderation. To assess such risks, this paper introduces a framework for auditing black-box NLP systems. Using the framework, we systematically evaluate five widely used commercial content moderation APIs. Analyzing five million queries based on four datasets, we find that APIs frequently rely on group identity terms, such as ``black'', to predict hate speech. While OpenAI's and Amazon's services perform slightly better, all providers under-moderate implicit hate speech, which uses codified messages, especially against LGBTQIA+ individuals. Simultaneously, they over-moderate counter-speech, reclaimed slurs and content related to Black, LGBTQIA+, Jewish, and Muslim people. We recommend that API providers offer better guidance on API implementation and threshold setting and more transparency on their APIs' limitations. Warning: This paper contains offensive and hateful terms and concepts. We have chosen to reproduce these terms for reasons of transparency.}, language = {en} } @inproceedings{HartmannOueslatiStauferetal., author = {Hartmann, David and Oueslati, Amin and Staufer, Dimitri and Pohlmann, Lena and Munzert, Simon and Heuer, Hendrik}, title = {Lost in Moderation: How Commercial Content Moderation APIs Over- and Under-Moderate Group-Targeted Hate Speech and Linguistic Variations}, series = {Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems}, booktitle = {Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems}, publisher = {ACM}, address = {New York, NY, USA}, doi = {10.1145/3706598.3713998}, pages = {1 -- 26}, abstract = {Commercial content moderation APIs are marketed as scalable solutions to combat online hate speech. However, the reliance on these APIs risks both silencing legitimate speech, called over-moderation, and failing to protect online platforms from harmful speech, known as under-moderation. To assess such risks, this paper introduces a framework for auditing black-box NLP systems. Using the framework, we systematically evaluate five widely used commercial content moderation APIs. Analyzing five million queries based on four datasets, we find that APIs frequently rely on group identity terms, such as "black", to predict hate speech. While OpenAI's and Amazon's services perform slightly better, all providers under-moderate implicit hate speech, which uses codified messages, especially against LGBTQIA+ individuals. Simultaneously, they over-moderate counter-speech, reclaimed slurs and content related to Black, LGBTQIA+, Jewish, and Muslim people. We recommend that API providers offer better guidance on API implementation and threshold setting and more transparency on their APIs' limitations. Warning: This paper contains offensive and hateful terms and concepts. We have chosen to reproduce these terms for reasons of transparency.}, language = {en} } @article{RomanelloWalawenderHsuetal., author = {Romanello, Marina and Walawender, Maria and Hsu, Shih-Che and Moskeland, Annalyse and Palmeiro-Silva, Yasna and Scamman, Daniel and Smallcombe, James W and Abdullah, Sabah and Ades, Melanie and Al-Maruf, Abdullah and Ameli, Nadia and Angelova, Denitsa and Ayeb-Karlsson, Sonja and Ballester, Joan and Basaga{\~n}a, Xavier and Bechara, Hannah and Beggs, Paul J and Cai, Wenjia and Campbell-Lendrum, Diarmid and Charnley, Gina E C and Courtenay, Orin and Cross, Troy J and Dalin, Carole and Dasandi, Niheer and Dasgupta, Shouro and Davies, Michael and Eckelman, Matthew and Freyberg, Chris and Garcia Corral, Paulina and Gasparyan, Olga and Giguere, Joseph and Gordon-Strachan, Georgiana and Gumy, Sophie and Gunther, Samuel H and Hamilton, Ian and Hang, Yun and H{\"a}nninen, Risto and Hartinger, Stella and He, Kehan and Heidecke, Julian and Hess, Jeremy J and Jankin, Slava and Jay, Ollie and Pantera, Dafni Kalatzi and Kelman, Ilan and Kennard, Harry and Kiesewetter, Gregor and Kinney, Patrick and Kniveton, Dominic and Koubi, Vally and Kouznetsov, Rostislav and Lampard, Pete and Lee, Jason K W and Lemke, Bruno and Li, Bo and Linke, Andrew and Liu, Yang and Liu, Zhao and Lowe, Rachel and Ma, Siqi and Mabhaudhi, Tafadzwanashe and Maia, Carla and Markandya, Anil and Martin, Greta and Martinez-Urtaza, Jaime and Maslin, Mark and McAllister, Lucy and McMichael, Celia and Mi, Zhifu and Milner, James and Minor, Kelton and Minx, Jan and Mohajeri, Nahid and Momen, Natalie C and Moradi-Lakeh, Maziar and Morrisey, Karyn and Munzert, Simon and Murray, Kris A and Obradovich, Nick and Orgen, Papa and Otto, Matthias and Owfi, Fereidoon and Pearman, Olivia L and Pega, Frank and Pershing, Andrew J and Pinho-Gomes, Ana-Catarina and Ponmattam, Jamie and Rabbaniha, Mahnaz and Repke, Tim and Roa, Jorge and Robinson, Elizabeth and Rockl{\"o}v, Joacim and Rojas-Rueda, David and Ruiz-Cabrejos, Jorge and Rusticucci, Matilde and Salas, Renee N and San Jos{\´e} Plana, Adri{\`a} and Semenza, Jan C and Sherman, Jodi D and Shumake-Guillemot, Joy and Singh, Pratik and Sj{\"o}din, Henrik and Smith, Matthew R and Sofiev, Mikhail and Sorensen, Cecilia and Springmann, Marco and Stowell, Jennifer D and Tabatabaei, Meisam and Tartarini, Federico and Taylor, Jonathon and Tonne, Cathryn and Treskova, Marina and Trinanes, Joaquin A and Uppstu, Andreas and Valdes-Ortega, Nicolas and Wagner, Fabian and Watts, Nick and Whitcombe, Hannah and Wood, Richard and Yang, Pu and Zhang, Ying and Zhang, Shaohui and Zhang, Chi and Zhang, Shihui and Zhu, Qiao and Gong, Peng and Montgomery, Hugh and Costello, Anthony}, title = {The 2025 report of the Lancet Countdown on health and climate change: climate change action offers a lifeline}, series = {The Lancet}, volume = {406}, journal = {The Lancet}, number = {10521}, publisher = {Elsevier BV}, doi = {10.1016/S0140-6736(25)01919-1}, pages = {2804 -- 2857}, language = {en} }