Extraction and Compilation of Events and Sub-events from Twitter

Khurdiya, Arpit; Dey, Lipika; Mahajan, Diwakar; Verma, Ishan

doi:10.1109/wi-iat.2012.192

Cited by 12 publications

(15 citation statements)

References 8 publications

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“…Lee and Sumuya [12] utilized the collective experiences and crowd behaviors on Twitter to detect geo-social events. Khurdiya et al [13] proposed a framework based on Searching on Lucene with Replication (SOLR) and Conditional Random Field (CRF) which can identify small sub-events around a major event and build a map of them. Rill et al [3] presented a system that uses special sentiment hashtags to detect emerging political events.…”

Section: Related Workmentioning

confidence: 99%

Detecting and Tracking Significant Events for Individuals on Twitter by Monitoring the Evolution of Twitter Followership Networks

Tang

2020

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People publish tweets on Twitter to share everything from global news to their daily life. Abundant user-generated content makes Twitter become one of the major channels for people to obtain information about real-world events. Event detection techniques help to extract events from massive amounts of Twitter data. However, most existing techniques are based on Twitter information streams, which contain plenty of noise and polluted content that would affect the accuracy of the detecting result. In this article, we present an event discovery method based on the change of the user’s followers, which can detect the occurrences of significant events relevant to the particular user. We divide these events into categories according to the positive or negative effect on the specific user. Further, we observe the evolution of individuals’ followership networks and analyze the dynamics of networks. The results show that events have different effects on the evolution of different features of Twitter followership networks. Our findings may play an important role for realizing how patterns of social interaction are impacted by events and can be applied in fields such as public opinion monitoring, disaster warning, crisis management, and intelligent decision making.

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Section: Related Workmentioning

confidence: 99%

Detecting and Tracking Significant Events for Individuals on Twitter by Monitoring the Evolution of Twitter Followership Networks

Tang

2020

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“…Retrospective event detection (RED) involves the task of detecting major events from historical data. The historical data can either be clustered or classified to detect significant events that happened in the past [54].…”

Section: Retrospective Event Detection (Red)mentioning

confidence: 99%

“…For collecting data, most of the research studies use Streaming API [29,34,88,90], or Search API [27,59,61], whereas limited research studies use Firehose [30,54]. Access to Twitter Firehose is costly which might be one of the reasons it is not used commonly in research studies.…”

Section: Are Offeredmentioning

confidence: 99%

“…The second approach uses manual labeling. Ground-truth is created by selecting N random tweets from the dataset that can easily be labeled by humans [54,90]. The third approach uses clustering algorithm to segregate the tweets into clusters and then automatically labels these clusters through bursty features [45,51].…”

Section: Benchmarking Datasetmentioning

confidence: 99%

“…Despite the limitations of Accuracy as an evaluation measure, we found that it is widely used in evaluating event detection methods [10,30,38,46,54,66,103]. It evaluates performance of the classifier as Accuracy = (TP+TN)/(TP+FP+TN+FN).…”

Section: Evaluation Measuresmentioning

confidence: 99%

See 2 more Smart Citations

What’s Happening Around the World? A Survey and Framework on Event Detection Techniques on Twitter

et al. 2019

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In the last few years, Twitter has become a popular platform for sharing opinions, experiences, news, and views in real-time. Twitter presents an interesting opportunity for detecting events happening around the world. The content (tweets) published on Twitter are short and pose diverse challenges for detecting and interpreting event-related information. This article provides insight into ongoing research. It explores recent research trends and techniques for event detection using Twitter data. We classify techniques and methodologies according to event types, orientation of content, event detection tasks, their evaluation, and common practices. We highlight the limitations of existing techniques and accordingly propose solutions

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Identification of Relevant Hashtags for Planned Events Using Learning to Rank

Madisetty

Desarkar

2019

Communications in Computer and Information Science

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Extraction and Compilation of Events and Sub-events from Twitter

Cited by 12 publications

References 8 publications

Detecting and Tracking Significant Events for Individuals on Twitter by Monitoring the Evolution of Twitter Followership Networks

Detecting and Tracking Significant Events for Individuals on Twitter by Monitoring the Evolution of Twitter Followership Networks

What’s Happening Around the World? A Survey and Framework on Event Detection Techniques on Twitter

Identification of Relevant Hashtags for Planned Events Using Learning to Rank

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