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In this paper, we investigate which indicators are most helpful in automatically detecting risk in Instagram private conversations, with an eye on high-level metadata, which will still be available in the scenario of end-to-end encryption. Toward this end, we collected Instagram data from 172 youth (ages 13-21) and asked them to identify private message conversations that made them feel uncomfortable or unsafe. Our participants risk-flagged 28,725 conversations that contained 4,181,970 direct messages, including textual posts and images. Based on this rich and multimodal dataset, we tested multiple feature sets (metadata, linguistic cues, and image features) and trained classifiers to detect risky conversations. Overall, we found that the metadata features (e.g., conversation length, a proxy for participant engagement) were the best predictors of risky conversations. However, for distinguishing between risk types, the different linguistic and media cues were the best predictors. Based on our findings, we provide design implications for AI risk detection systems in the presence of end-to-end encryption. More broadly, our work contributes to the literature on adolescent online safety by moving toward more robust solutions for risk detection that directly takes into account the lived risk experiences of youth.<\/jats:p>","DOI":"10.1145\/3579608","type":"journal-article","created":{"date-parts":[[2023,4,16]],"date-time":"2023-04-16T17:23:07Z","timestamp":1681665787000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":30,"title":["Getting Meta: A Multimodal Approach for Detecting Unsafe Conversations within Instagram Direct Messages of Youth"],"prefix":"10.1145","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7520-5279","authenticated-orcid":false,"given":"Shiza","family":"Ali","sequence":"first","affiliation":[{"name":"Boston University, Boston, MA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5829-8004","authenticated-orcid":false,"given":"Afsaneh","family":"Razi","sequence":"additional","affiliation":[{"name":"Drexel University, Philadelphia, PA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2481-2292","authenticated-orcid":false,"given":"Seunghyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, GA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1569-9662","authenticated-orcid":false,"given":"Ashwaq","family":"Alsoubai","sequence":"additional","affiliation":[{"name":"Vanderbilt University, Nashville, TN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5082-7557","authenticated-orcid":false,"given":"Chen","family":"Ling","sequence":"additional","affiliation":[{"name":"Boston University, Boston, MA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8939-264X","authenticated-orcid":false,"given":"Munmun","family":"De Choudhury","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, GA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6223-1029","authenticated-orcid":false,"given":"Pamela J.","family":"Wisniewski","sequence":"additional","affiliation":[{"name":"Vanderbilt University, Nashville, TN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6162-578X","authenticated-orcid":false,"given":"Gianluca","family":"Stringhini","sequence":"additional","affiliation":[{"name":"Boston University, Boston, MA, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,4,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3334480.3383073"},{"key":"e_1_2_1_2_1","volume-title":"Pamela J Wisniewski, and Gianluca Stringhini.","author":"Ali Shiza","year":"2022","unstructured":"Shiza Ali, Afsaneh Razi, Seunghyun Kim, Ashwaq Alsoubai, Joshua Gracie, Munmun De Choudhury, Pamela J Wisniewski, and Gianluca Stringhini. 2022. 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