{"id":{"repo_id":"alabama","oai_identifier":"oai:ir.ua.edu:123456789/17996"},"canonical_url":"https://search.dev.ndltd.org/etd/alabama/oai:ir.ua.edu:123456789/17996","repository":{"repo_id":"alabama","name":"University of Alabama","base_url":"https://ir-api.ua.edu/oai/request"},"display":{"title":"Self-Reported Arrest and Dark Web Use: an Examination of Offline Risk Factors","abstract":"Research into Dark Web user characteristics is limited, and there is still much to be explored, including potential offline risk factors for online risky behavior. This study examines secondary data to determine whether self-reported arrest predicts Dark Web use, hypothesizing that individuals who report prior arrest will be more likely to report accessing the Dark Web. Using data from an original study (n= 1,793), stepwise logistic regression was utilized to assess the association between self-reported arrest and Dark Web use, controlling for variables such as low self-control, age, gender, race/ethnicity, income, and education level. Self-reported arrest was found to have a statistically significant and positive association with Dark Web use, with individuals who report prior arrest being twice as likely to report accessing the Dark Web within the past 12 months. Low self-control, younger age, being male, having a higher level of education, and race/ethnicity were also significantly associated with Dark Web use. These findings provide new insights into characteristics of Dark Web users and suggest that prior arrest is associated with use of this risky online platform.","abstract_html":"Research into Dark Web user characteristics is limited, and there is still much to be explored, including potential offline risk factors for online risky behavior. This study examines secondary data to determine whether self-reported arrest predicts Dark Web use, hypothesizing that individuals who report prior arrest will be more likely to report accessing the Dark Web. Using data from an original study (n= 1,793), stepwise logistic regression was utilized to assess the association between self-reported arrest and Dark Web use, controlling for variables such as low self-control, age, gender, race/ethnicity, income, and education level. Self-reported arrest was found to have a statistically significant and positive association with Dark Web use, with individuals who report prior arrest being twice as likely to report accessing the Dark Web within the past 12 months. Low self-control, younger age, being male, having a higher level of education, and race/ethnicity were also significantly associated with Dark Web use. These findings provide new insights into characteristics of Dark Web users and suggest that prior arrest is associated with use of this risky online platform.","abstract_has_math":false,"creators":["Graziano, Sarah Kaye"],"institution":"University of Alabama Libraries","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Dickinson, Timothy","Belshaw, Scott H."],"advisors":["Partin, Raymond D."],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026","date_published":"2026","updated_at":"2026-07-27T18:44:18Z","subjects":["arrest","Dark Web","low self-control","Lucid","survey"],"languages":["en_US","English"],"rights":["All rights reserved by the author unless otherwise indicated."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["1228768"],"render_values":[{"text":"1228768","href":null,"code":true}]}]},"links":{"outbound_url":"https://ir.ua.edu/handle/123456789/17996","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dickinson, Timothy","Belshaw, Scott H."]},{"key":"dc:contributor.advisor","label":"Advisor","values":["Partin, Raymond D."]},{"key":"dc:creator","label":"Author","values":["Graziano, Sarah Kaye"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-07-09T13:30:49Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-07-09T13:30:49Z"]},{"key":"dc:date.issued","label":"Date","values":["2026"]},{"key":"dc:publisher","label":"Institution","values":["University of Alabama Libraries"]},{"key":"dc:type","label":"Dc Type","values":["thesis","text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["arrest","Dark Web","low self-control","Lucid","survey"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]},{"key":"dc:rights","label":"Dc Rights","values":["All rights reserved by the author unless otherwise indicated."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["1228768"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://ir.ua.edu/handle/123456789/17996"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Electronic Thesis or Dissertation"]},{"key":"dc:description.abstract","label":"Abstract","values":["Research into Dark Web user characteristics is limited, and there is still much to be explored, including potential offline risk factors for online risky behavior. This study examines secondary data to determine whether self-reported arrest predicts Dark Web use, hypothesizing that individuals who report prior arrest will be more likely to report accessing the Dark Web. Using data from an original study (n= 1,793), stepwise logistic regression was utilized to assess the association between self-reported arrest and Dark Web use, controlling for variables such as low self-control, age, gender, race/ethnicity, income, and education level. Self-reported arrest was found to have a statistically significant and positive association with Dark Web use, with individuals who report prior arrest being twice as likely to report accessing the Dark Web within the past 12 months. Low self-control, younger age, being male, having a higher level of education, and race/ethnicity were also significantly associated with Dark Web use. These findings provide new insights into characteristics of Dark Web users and suggest that prior arrest is associated with use of this risky online platform."]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["electronic"]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Self-Reported Arrest and Dark Web Use: an Examination of Offline Risk Factors"]}]}],"canonical_facts":{"dc:contributor":["Dickinson, Timothy","Belshaw, Scott H."],"dc:contributor.advisor":["Partin, Raymond D."],"dc:creator":["Graziano, Sarah Kaye"],"dc:date.accessioned":["2026-07-09T13:30:49Z"],"dc:date.available":["2026-07-09T13:30:49Z"],"dc:date.issued":["2026"],"dc:description":["Electronic Thesis or Dissertation"],"dc:description.abstract":["Research into Dark Web user characteristics is limited, and there is still much to be explored, including potential offline risk factors for online risky behavior. This study examines secondary data to determine whether self-reported arrest predicts Dark Web use, hypothesizing that individuals who report prior arrest will be more likely to report accessing the Dark Web. Using data from an original study (n= 1,793), stepwise logistic regression was utilized to assess the association between self-reported arrest and Dark Web use, controlling for variables such as low self-control, age, gender, race/ethnicity, income, and education level. Self-reported arrest was found to have a statistically significant and positive association with Dark Web use, with individuals who report prior arrest being twice as likely to report accessing the Dark Web within the past 12 months. Low self-control, younger age, being male, having a higher level of education, and race/ethnicity were also significantly associated with Dark Web use. 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