{"title":"conteNXt:基于图的方法,在 OSN 中吸收内容和上下文以进行事件检测","authors":"Sielvie Sharma;Muhammad Abulaish;Tanvir Ahmad","doi":"10.1109/TCSS.2024.3372399","DOIUrl":null,"url":null,"abstract":"Social networks are rapidly expanding due to their imperative role in disseminating information in a split second, emerging as the primary source for breaking news. As a result, the rich, user-generated information entices researchers to delve deeper and extract valuable insights. Event detection in online social networks (OSNs) is a research problem that has shifted researchers attention from traditional news media to online social media data. Event detection in OSNs is an automated process, addressing the impractical task of manually filtering potential events from vast amounts of online data. Unfortunately, the informality and semantic sparsity of online social networking text pose significant challenges to the event detection task. To this end, we present an approach named \n<monospace>conteNXt</monospace>\n for detecting events from Twitter (currently “X”) posts (also known as Tweets). To handle large amounts of data, the proposed method divides tweets into bins and uses postprocessing methods to extract bursty keyphrases. These keyphrases are then used to generate a weighted keyphrase graph using the Word2Vec model. Finally, Markov clustering is employed to cluster and detect events in the bursty keyphrase graph. \n<monospace>conteNXt</monospace>\n is evaluated on the \n<monospace>EventCorpus2012</monospace>\n benchmark dataset and two additional datasets extracted from the archive, \n<monospace>Archive2020</monospace>\n and \n<monospace>Archive2021</monospace>\n, using performance evaluation metrics: #events, precision, recall, and F1-score. The proposed approach outperforms state-of-the-art methods, including \n<monospace>SEDTWik</monospace>\n, \n<monospace>Twevent</monospace>\n, \n<monospace>Sentence-BERT</monospace>\n, \n<monospace>MABED</monospace>\n, \n<monospace>EDED</monospace>\n, \n<monospace>CommunityINDICATOR</monospace>\n, and \n<monospace>EventX</monospace>\n. Additionally, the proposed approach is capable of detecting vital events that are not identified by the aforementioned state-of-the-art methods. \n<uri>https://github.com/Sielvi/conteNXt</uri>","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"conteNXt: A Graph-Based Approach to Assimilate Content and Context for Event Detection in OSN\",\"authors\":\"Sielvie Sharma;Muhammad Abulaish;Tanvir Ahmad\",\"doi\":\"10.1109/TCSS.2024.3372399\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Social networks are rapidly expanding due to their imperative role in disseminating information in a split second, emerging as the primary source for breaking news. As a result, the rich, user-generated information entices researchers to delve deeper and extract valuable insights. Event detection in online social networks (OSNs) is a research problem that has shifted researchers attention from traditional news media to online social media data. Event detection in OSNs is an automated process, addressing the impractical task of manually filtering potential events from vast amounts of online data. Unfortunately, the informality and semantic sparsity of online social networking text pose significant challenges to the event detection task. To this end, we present an approach named \\n<monospace>conteNXt</monospace>\\n for detecting events from Twitter (currently “X”) posts (also known as Tweets). To handle large amounts of data, the proposed method divides tweets into bins and uses postprocessing methods to extract bursty keyphrases. These keyphrases are then used to generate a weighted keyphrase graph using the Word2Vec model. Finally, Markov clustering is employed to cluster and detect events in the bursty keyphrase graph. \\n<monospace>conteNXt</monospace>\\n is evaluated on the \\n<monospace>EventCorpus2012</monospace>\\n benchmark dataset and two additional datasets extracted from the archive, \\n<monospace>Archive2020</monospace>\\n and \\n<monospace>Archive2021</monospace>\\n, using performance evaluation metrics: #events, precision, recall, and F1-score. The proposed approach outperforms state-of-the-art methods, including \\n<monospace>SEDTWik</monospace>\\n, \\n<monospace>Twevent</monospace>\\n, \\n<monospace>Sentence-BERT</monospace>\\n, \\n<monospace>MABED</monospace>\\n, \\n<monospace>EDED</monospace>\\n, \\n<monospace>CommunityINDICATOR</monospace>\\n, and \\n<monospace>EventX</monospace>\\n. Additionally, the proposed approach is capable of detecting vital events that are not identified by the aforementioned state-of-the-art methods. \\n<uri>https://github.com/Sielvi/conteNXt</uri>\",\"PeriodicalId\":13044,\"journal\":{\"name\":\"IEEE Transactions on Computational Social Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2024-03-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Computational Social Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10485393/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, CYBERNETICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Computational Social Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10485393/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
conteNXt: A Graph-Based Approach to Assimilate Content and Context for Event Detection in OSN
Social networks are rapidly expanding due to their imperative role in disseminating information in a split second, emerging as the primary source for breaking news. As a result, the rich, user-generated information entices researchers to delve deeper and extract valuable insights. Event detection in online social networks (OSNs) is a research problem that has shifted researchers attention from traditional news media to online social media data. Event detection in OSNs is an automated process, addressing the impractical task of manually filtering potential events from vast amounts of online data. Unfortunately, the informality and semantic sparsity of online social networking text pose significant challenges to the event detection task. To this end, we present an approach named
conteNXt
for detecting events from Twitter (currently “X”) posts (also known as Tweets). To handle large amounts of data, the proposed method divides tweets into bins and uses postprocessing methods to extract bursty keyphrases. These keyphrases are then used to generate a weighted keyphrase graph using the Word2Vec model. Finally, Markov clustering is employed to cluster and detect events in the bursty keyphrase graph.
conteNXt
is evaluated on the
EventCorpus2012
benchmark dataset and two additional datasets extracted from the archive,
Archive2020
and
Archive2021
, using performance evaluation metrics: #events, precision, recall, and F1-score. The proposed approach outperforms state-of-the-art methods, including
SEDTWik
,
Twevent
,
Sentence-BERT
,
MABED
,
EDED
,
CommunityINDICATOR
, and
EventX
. Additionally, the proposed approach is capable of detecting vital events that are not identified by the aforementioned state-of-the-art methods.
https://github.com/Sielvi/conteNXt
期刊介绍:
IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.