{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/90884"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/90884","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Analysis of Moving Events Using Tweets","abstract":"The Digital Library Research Laboratory (DLRL) has collected over 3.5 billion tweets on different events for the Coordinated, Behaviorally-Aware Recovery for Transportation and Power Disruptions (CBAR-tpd), the Integrated Digital Event Archiving and Library (IDEAL), and the Global Event Trend Archive Research (GETAR) projects. The tweet collection topics include heart attack, solar eclipse, terrorism, etc. There are several collections on naturally occurring events such as hurricanes, floods, and solar eclipses. Such naturally occurring events are distributed across space and time. It would be beneficial to researchers if we can perform a spatial-temporal analysis to test some hypotheses, and to find any trends that tweets would reveal for such events. I apply an existing algorithm to detect locations from tweets by modifying it to work better with the type of datasets I work with. I use the time captured in tweets and also identify the tense of the sentences in tweets to perform the temporal analysis. I build a rule-based model for obtaining the tense of a tweet. The results from these two algorithms are merged to analyze naturally occurring moving events such as solar eclipses and hurricanes. Using the spatial-temporal information from tweets, I study if tweets can be a relevant source of information in understanding the movement of the event. I create visualizations to compare the actual path of the event with the information extracted by my algorithms. After examining the results from the analysis, I noted that Twitter can be a reliable source to identify places affected by moving events almost immediately. The locations obtained are at a more detailed level than in news-wires. We can also identify the time that an event affected a particular region by date.","abstract_html":"The Digital Library Research Laboratory (DLRL) has collected over 3.5 billion tweets on different events for the Coordinated, Behaviorally-Aware Recovery for Transportation and Power Disruptions (CBAR-tpd), the Integrated Digital Event Archiving and Library (IDEAL), and the Global Event Trend Archive Research (GETAR) projects. The tweet collection topics include heart attack, solar eclipse, terrorism, etc. There are several collections on naturally occurring events such as hurricanes, floods, and solar eclipses. Such naturally occurring events are distributed across space and time. It would be beneficial to researchers if we can perform a spatial-temporal analysis to test some hypotheses, and to find any trends that tweets would reveal for such events. I apply an existing algorithm to detect locations from tweets by modifying it to work better with the type of datasets I work with. I use the time captured in tweets and also identify the tense of the sentences in tweets to perform the temporal analysis. I build a rule-based model for obtaining the tense of a tweet. The results from these two algorithms are merged to analyze naturally occurring moving events such as solar eclipses and hurricanes. Using the spatial-temporal information from tweets, I study if tweets can be a relevant source of information in understanding the movement of the event. I create visualizations to compare the actual path of the event with the information extracted by my algorithms. After examining the results from the analysis, I noted that Twitter can be a reliable source to identify places affected by moving events almost immediately. The locations obtained are at a more detailed level than in news-wires. We can also identify the time that an event affected a particular region by date.","abstract_has_math":false,"creators":["Patil, Supritha Basavaraj"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Computer Science and Applications","degree_department":"Computer Science","school":null,"contributors":[],"advisors":[],"committee_chairs":["Fox, Edward A."],"committee_members":["Lee, Sunshin","Prakash, B. Aditya"],"year":2019,"date_issued":"2019-07-02","date_published":"2019-07-02","updated_at":"2026-07-22T22:19:57Z","subjects":["Natural Language Processing","Twitter"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:21279"],"render_values":[{"text":"vt_gsexam:21279","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/90884","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Fox, Edward A."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Lee, Sunshin","Prakash, B. Aditya"]},{"key":"dc:contributor.department","label":"Department","values":["Computer Science"]},{"key":"dc:creator","label":"Author","values":["Patil, Supritha Basavaraj"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2019-07-03T08:01:50Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2019-07-03T08:01:50Z"]},{"key":"dc:date.issued","label":"Date","values":["2019-07-02"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science and Applications"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Natural Language Processing","Twitter"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:21279"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/90884"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The Digital Library Research Laboratory (DLRL) has collected over 3.5 billion tweets on different events for the Coordinated, Behaviorally-Aware Recovery for Transportation and Power Disruptions (CBAR-tpd), the Integrated Digital Event Archiving and Library (IDEAL), and the Global Event Trend Archive Research (GETAR) projects. The tweet collection topics include heart attack, solar eclipse, terrorism, etc. There are several collections on naturally occurring events such as hurricanes, floods, and solar eclipses. Such naturally occurring events are distributed across space and time. It would be beneficial to researchers if we can perform a spatial-temporal analysis to test some hypotheses, and to find any trends that tweets would reveal for such events. I apply an existing algorithm to detect locations from tweets by modifying it to work better with the type of datasets I work with. I use the time captured in tweets and also identify the tense of the sentences in tweets to perform the temporal analysis. I build a rule-based model for obtaining the tense of a tweet. The results from these two algorithms are merged to analyze naturally occurring moving events such as solar eclipses and hurricanes. Using the spatial-temporal information from tweets, I study if tweets can be a relevant source of information in understanding the movement of the event. I create visualizations to compare the actual path of the event with the information extracted by my algorithms. After examining the results from the analysis, I noted that Twitter can be a reliable source to identify places affected by moving events almost immediately. The locations obtained are at a more detailed level than in news-wires. We can also identify the time that an event affected a particular region by date."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["News now travels faster on social media than through news channels. Information from social media can help retrieve minute details that might not be emphasized in news. People tend to describe their actions or sentiments in tweets. I aim at studying if such collections of tweets are dependable sources for identifying paths of moving events. In events like hurricanes, using Twitter can help in analyzing people’s reaction to such moving events. These may include actions such as dislocation or emotions during different phases of the event. The results obtained in the experiments concur with the actual path of the events with respect to the regions affected and time. The frequency of tweets increases during event peaks. The number of locations affected that are identified are significantly more than in news wires."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Analysis of Moving Events Using Tweets"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Fox, Edward A."],"dc:contributor.committeemember":["Lee, Sunshin","Prakash, B. Aditya"],"dc:contributor.department":["Computer Science"],"dc:creator":["Patil, Supritha Basavaraj"],"dc:date.accessioned":["2019-07-03T08:01:50Z"],"dc:date.available":["2019-07-03T08:01:50Z"],"dc:date.issued":["2019-07-02"],"dc:description.abstract":["The Digital Library Research Laboratory (DLRL) has collected over 3.5 billion tweets on different events for the Coordinated, Behaviorally-Aware Recovery for Transportation and Power Disruptions (CBAR-tpd), the Integrated Digital Event Archiving and Library (IDEAL), and the Global Event Trend Archive Research (GETAR) projects. The tweet collection topics include heart attack, solar eclipse, terrorism, etc. There are several collections on naturally occurring events such as hurricanes, floods, and solar eclipses. Such naturally occurring events are distributed across space and time. It would be beneficial to researchers if we can perform a spatial-temporal analysis to test some hypotheses, and to find any trends that tweets would reveal for such events. I apply an existing algorithm to detect locations from tweets by modifying it to work better with the type of datasets I work with. I use the time captured in tweets and also identify the tense of the sentences in tweets to perform the temporal analysis. I build a rule-based model for obtaining the tense of a tweet. The results from these two algorithms are merged to analyze naturally occurring moving events such as solar eclipses and hurricanes. Using the spatial-temporal information from tweets, I study if tweets can be a relevant source of information in understanding the movement of the event. I create visualizations to compare the actual path of the event with the information extracted by my algorithms. After examining the results from the analysis, I noted that Twitter can be a reliable source to identify places affected by moving events almost immediately. The locations obtained are at a more detailed level than in news-wires. We can also identify the time that an event affected a particular region by date."],"dc:description.abstractgeneral":["News now travels faster on social media than through news channels. Information from social media can help retrieve minute details that might not be emphasized in news. People tend to describe their actions or sentiments in tweets. I aim at studying if such collections of tweets are dependable sources for identifying paths of moving events. In events like hurricanes, using Twitter can help in analyzing people’s reaction to such moving events. These may include actions such as dislocation or emotions during different phases of the event. The results obtained in the experiments concur with the actual path of the events with respect to the regions affected and time. The frequency of tweets increases during event peaks. The number of locations affected that are identified are significantly more than in news wires."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:21279"],"dc:identifier.uri":["http://hdl.handle.net/10919/90884"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Natural Language Processing","Twitter"],"dc:title":["Analysis of Moving Events Using Tweets"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science and Applications"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:57Z"}