{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/49616"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/49616","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Social sensing games","abstract":"We introduce Social Sensing Games (SSGs), a new method for collecting data about social relationships, and present an algorithm that can be used to efficiently infer information from the output of the games. The main purpose of this new method is to use people's online interactions to learn about their offline behavior. Traditionally, scientists have studied social interactions through the use social networks obtained through carefully designed self-report surveys that impose limitations on the types of research questions that can be answered. Recently, thanks to the ever-increasing use of computers and mobile devices for managing social relationships, scientists look to use large amounts of data that is easily accessible. Unfortunately, this latter data lacks the experimental and theoretical validity of previous methods. SSGs address these concerns by providing an interface that can collect fine-grained data that is relevant to the research question at hand. The first contribution of this thesis is the formalization of Social Sensing Games in such a way that they combine the power of lab-controlled experiments, the detailed observations of observational studies, and the scalability and inferential power of computational methods. This new definition can be used by researchers to easily design SSGs specific to the problem they wish to address. The second contribution is most relevant to the field of social network analysis. We present an algorithm for analyzing the output of SSGs which main insight is that, in some cases, pairwise relationships are enough to infer global attributes of the nodes encoded in a social network and that such assumption may reduce the complexity of inference, help with the scarcity of data, and still maintain some of the context of the network. We show these contributions through two applications: The evaluation of commonsense knowledge, and the identification of classroom aggressors (or bullying). The second application being in itself an important contribution that provides new insights concerning the study of bullying and cyberbullying.","abstract_html":"We introduce Social Sensing Games (SSGs), a new method for collecting data about social relationships, and present an algorithm that can be used to efficiently infer information from the output of the games. The main purpose of this new method is to use people's online interactions to learn about their offline behavior. Traditionally, scientists have studied social interactions through the use social networks obtained through carefully designed self-report surveys that impose limitations on the types of research questions that can be answered. Recently, thanks to the ever-increasing use of computers and mobile devices for managing social relationships, scientists look to use large amounts of data that is easily accessible. Unfortunately, this latter data lacks the experimental and theoretical validity of previous methods. SSGs address these concerns by providing an interface that can collect fine-grained data that is relevant to the research question at hand. The first contribution of this thesis is the formalization of Social Sensing Games in such a way that they combine the power of lab-controlled experiments, the detailed observations of observational studies, and the scalability and inferential power of computational methods. This new definition can be used by researchers to easily design SSGs specific to the problem they wish to address. The second contribution is most relevant to the field of social network analysis. We present an algorithm for analyzing the output of SSGs which main insight is that, in some cases, pairwise relationships are enough to infer global attributes of the nodes encoded in a social network and that such assumption may reduce the complexity of inference, help with the scarcity of data, and still maintain some of the context of the network. We show these contributions through two applications: The evaluation of commonsense knowledge, and the identification of classroom aggressors (or bullying). The second application being in itself an important contribution that provides new insights concerning the study of bullying and cyberbullying.","abstract_has_math":false,"creators":["Mancilla Caceres, Juan F."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Amir, Eyal","Espelage, Dorothy L.","Girju, Roxana","Karahalios, Karrie G.","Lieberman, Henry"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-05-30T16:52:33Z","date_published":"2014-05-30T16:52:33Z","updated_at":"2026-07-22T22:25:38Z","subjects":["Game-Based Methods","Social Network Analysis","Identification of Bullying and Cyberbullying","Evaluation of Commonsense Knowledge","Formalization of Social Sensing through Games","Global Inference from Pairwise Interactions","Visualizing Pairwise Interactions","Combining observational methods with lab-controlled experiments in a computational setting"],"languages":["en"],"rights":["Copyright 2014 Juan Fernando Mancilla Caceres"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/49616","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Amir, Eyal","Espelage, Dorothy L.","Girju, Roxana","Karahalios, Karrie G.","Lieberman, Henry"]},{"key":"dc:creator","label":"Author","values":["Mancilla Caceres, Juan F."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-05-30T16:52:33Z","2014-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Game-Based Methods","Social Network Analysis","Identification of Bullying and Cyberbullying","Evaluation of Commonsense Knowledge","Formalization of Social Sensing through Games","Global Inference from Pairwise Interactions","Visualizing Pairwise Interactions","Combining observational methods with lab-controlled experiments in a computational setting"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2014 Juan Fernando Mancilla Caceres"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/49616"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["We introduce Social Sensing Games (SSGs), a new method for collecting data about social relationships, and present an algorithm that can be used to efficiently infer information from the output of the games. 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The first contribution of this thesis is the formalization of Social Sensing Games in such a way that they combine the power of lab-controlled experiments, the detailed observations of observational studies, and the scalability and inferential power of computational methods. This new definition can be used by researchers to easily design SSGs specific to the problem they wish to address. The second contribution is most relevant to the field of social network analysis. We present an algorithm for analyzing the output of SSGs which main insight is that, in some cases, pairwise relationships are enough to infer global attributes of the nodes encoded in a social network and that such assumption may reduce the complexity of inference, help with the scarcity of data, and still maintain some of the context of the network. We show these contributions through two applications: The evaluation of commonsense knowledge, and the identification of classroom aggressors (or bullying). 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This new definition can be used by researchers to easily design SSGs specific to the problem they wish to address. The second contribution is most relevant to the field of social network analysis. We present an algorithm for analyzing the output of SSGs which main insight is that, in some cases, pairwise relationships are enough to infer global attributes of the nodes encoded in a social network and that such assumption may reduce the complexity of inference, help with the scarcity of data, and still maintain some of the context of the network. We show these contributions through two applications: The evaluation of commonsense knowledge, and the identification of classroom aggressors (or bullying). 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