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However, their invisible interventions might also pose harm to these connections. Dating platforms offer a prime example where, despite extensive research on human-inflicted harm, the potential harm from the algorithms themselves, and user strategies for mitigating them, remains largely unexplored. In our analysis of 7,043 reviews and interviews with 30 Tinder users, we unveiled how users perceive algorithmic harm as damaging self-esteem, sabotaging potential relationships, encouraging antisocial behavior, and misrepresenting or marginalizing certain identities. We introduce a new algorithmic folk theory, the\n            <jats:italic toggle=\"yes\">\"conflict of interest\"<\/jats:italic>\n            theory, perceived to perpetuate these harms. This theory encapsulates users' sense of a contradiction between the dating platform's promise of finding the perfect partner (leading to discontinued use of Tinder) and its commercial interest in retaining users to increase revenue. Users suspected various algorithmic processes pursuant to this theory, such as (a) throttling profile visibility, (b) manipulating users' matches, and (c) recommending large quantities of profiles that will not lead to matches. They also described various strategies in resistance or defense of these suspected algorithmic processes, such as engaging in counter-intuitive behaviour to disrupt the unfavorable algorithmic processes or leveraging location based filtering for match variety and safety. We conclude by discussing how the perceived algorithmic harms can inform the development of new algorithmic implementations that balance both user and company interests.\n          <\/jats:p>","DOI":"10.1145\/3689710","type":"journal-article","created":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T15:52:40Z","timestamp":1731081160000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["When the \"Matchmaker\" Does Not Have Your Interest at Heart: Perceived Algorithmic Harms, Folk Theories, and Users' Counter-Strategies on Tinder"],"prefix":"10.1145","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5365-4695","authenticated-orcid":false,"given":"Fatemeh","family":"Alizadeh","sequence":"first","affiliation":[{"name":"Information Systems, University of Siegen, Siegen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2848-4409","authenticated-orcid":false,"given":"Dennis","family":"Lawo","sequence":"additional","affiliation":[{"name":"Institut f\u00fcr Verbraucherinformatik, Bonn-Rhein Sieg University of Applied Science, Sankt Augustin, NRW, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7785-5061","authenticated-orcid":false,"given":"Gunnar","family":"Stevens","sequence":"additional","affiliation":[{"name":"Information Systems, University of Siegen, Siegen, NRW, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6854-5336","authenticated-orcid":false,"given":"Douglas","family":"Zytko","sequence":"additional","affiliation":[{"name":"College of Innovation &amp; Technology, University of Michigan-Flint, Flint, Michigan, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1499-3045","authenticated-orcid":false,"given":"Motahhare","family":"Eslami","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,11,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.24251\/HICSS.2022.351"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/HICSS.2016.240"},{"key":"e_1_2_1_3_1","unstructured":"UK National Crime Agency. 2022. 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