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The social media platform has been renamed to 'X,' however our study data is from the time-period prior to the name change., developing an ''unexpectedness quotient'' to quantify deviations from predicted engagement levels. Our analysis of over 600,000 tweets reveals distinct patterns in how content characteristics influence unexpected engagement. News, politics, and business tweets receive more retweets and comments than expected, suggesting users prioritize sharing and discussing informational content. In contrast, games and sports-related topics garner unexpected likes and comments, indicating higher emotional investment in these domains. The relationship between content attributes and engagement types follows clear patterns: subjective tweets attract more likes while objective tweets receive more retweets, and longer, complex tweets with URLs unexpectedly receive more retweets. These findings demonstrate how users employ different engagement types as signals of varying strength based on content characteristics, and how certain content types more effectively compete for attention in the social media ecosystem. Our results offer valuable insights for content creators optimizing engagement strategies, platform designers facilitating meaningful interactions, and researchers studying online social behavior.<\/jats:p>","DOI":"10.1145\/3757657","type":"journal-article","created":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T16:59:10Z","timestamp":1760633950000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Signals in the Noise: Decoding Unexpected Engagement Patterns on Twitter"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4743-8360","authenticated-orcid":false,"given":"Yulin","family":"Yu","sequence":"first","affiliation":[{"name":"University of Michigan, Ann Arbor, Michigan, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7652-5485","authenticated-orcid":false,"given":"Houming","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Michigan, Ann Arbor, Michigan, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8351-3463","authenticated-orcid":false,"given":"Daniel","family":"Romero","sequence":"additional","affiliation":[{"name":"University of Michigan, Ann Arbor, Michigan, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0994-9488","authenticated-orcid":false,"given":"Paramveer S.","family":"Dhillon","sequence":"additional","affiliation":[{"name":"University of Michigan, Ann Arbor, Michigan, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,10,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v13i01.3208"},{"key":"e_1_2_1_2_1","volume-title":"Mostly harmless econometrics: An empiricist's companion","author":"Angrist Joshua D","unstructured":"Joshua D Angrist and J\u00f6rn-Steffen Pischke. 2008. 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