{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/86656"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/86656","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Decision Modeling of the Spread of Rumors on Online Social Media","abstract":"M.S.","abstract_html":"M.S.","abstract_has_math":false,"creators":["Dimitrov, Antonio"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Zhuang, Jun","Industrial and Systems Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-02-21T21:36:09Z","date_published":"2025-02-21T21:36:09Z","updated_at":"2026-07-27T19:05:34Z","subjects":["operations research"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/86656","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhuang, Jun","Industrial and Systems Engineering"]},{"key":"dc:creator","label":"Author","values":["Dimitrov, Antonio"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-02-21T21:36:09Z","2020"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["operations research"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/86656"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["M.S.","Social media has rapidly expanded in a relatively short period of time and there are few restrictions on what can be shared. When the limited restrictions and rapid expansion are paired with the growing role that social media plays in our everyday lives, they can be a recipe for disaster. Although social media can be extremely beneficial, the spreading of misinformation can be devastating. Eliminating the spread of misinformation has the potential to create economic benefits, transform social media to a more reliable news source, and potentially even save lives along with other unnamed perks. Current methods of stopping the spread of misinformation revolve around users or official accounts debunking rumors that they come across and using rumor control pages for larger scale events. The research conducted fills a research gap by including different user profiles, using actual data that was collected, using different sharing/ not-sharing and true, false, or other probabilities, as well as a utility function that incorporates all of these elements. By having different probabilities that a rumor is true, false, or other based on the rumor case, we are also able to assign probabilities that the followers of that user will spread that rumor. The results show the impact that different factors had on the overall utility and which factors were the most important. The effectiveness of similar rumors that are spread within a short time of each other was also shown to be much lower for the latter rumor.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Decision Modeling of the Spread of Rumors on Online Social Media"]}]}],"canonical_facts":{"dc:contributor":["Zhuang, Jun","Industrial and Systems Engineering"],"dc:creator":["Dimitrov, Antonio"],"dc:date":["2025-02-21T21:36:09Z","2020"],"dc:description":["M.S.","Social media has rapidly expanded in a relatively short period of time and there are few restrictions on what can be shared. When the limited restrictions and rapid expansion are paired with the growing role that social media plays in our everyday lives, they can be a recipe for disaster. Although social media can be extremely beneficial, the spreading of misinformation can be devastating. Eliminating the spread of misinformation has the potential to create economic benefits, transform social media to a more reliable news source, and potentially even save lives along with other unnamed perks. Current methods of stopping the spread of misinformation revolve around users or official accounts debunking rumors that they come across and using rumor control pages for larger scale events. The research conducted fills a research gap by including different user profiles, using actual data that was collected, using different sharing/ not-sharing and true, false, or other probabilities, as well as a utility function that incorporates all of these elements. By having different probabilities that a rumor is true, false, or other based on the rumor case, we are also able to assign probabilities that the followers of that user will spread that rumor. The results show the impact that different factors had on the overall utility and which factors were the most important. The effectiveness of similar rumors that are spread within a short time of each other was also shown to be much lower for the latter rumor.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/86656"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["operations research"],"dc:title":["Decision Modeling of the Spread of Rumors on Online Social Media"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T19:05:34Z"}