Event identification for social streams using keyword-based evolving graph sequences

Kwan, Elizabeth; Hsu, Pei-Ling; Liang, Jheng-He; Chen, Yi-Shin

doi:10.1145/2492517.2492528

Cited by 12 publications

(5 citation statements)

References 9 publications

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“…However, a single tweet can have multiple hashtags and usually the number of tweets associated with a hashtag is relatively very small compared to the huge volume of tweets published per day (Ishikawa et al, 2012). Besides that, platform's features for single SN are huge and complex and employing such features has been always a major challenge for many scientists (Kwan et al, 2013). Moreover, such features could be missing or not trustworthy (Goswami and Kumar, 2016).…”

Section: Clustering-based Methodsmentioning

confidence: 99%

“…This method has the issue of defining a proper threshold for purring, where various values can lead to different results. Kwan et al (2013) built a directed weighted graph from the keywords that were extracted from different time windows. Later, they implemented cut off technique to identify three types of events e.g., one shot, long run and non-events.…”

Section: Graph-based Clustering Methodsmentioning

confidence: 99%

“…Different from Twitter, some SNs (e.g., Facebook) have privacy issues that limit the collection process to just offline publicly available data that have been given permissions to be collected (Chen et al, 2016). Becker et al (2011a;Phuvipadawat and Murata, 2010;Sakaki et al, 2010;Popescu et al, 2011;Ritter et al, 2012;Li et al, 2012a;Weng and Lee, 2011;Sankaranarayanan et al, 2009;Culotta, 2010;Osborne et al, 2012;Subašić and Berendt, 2011;Becker et al, 2012;Abhik and Toshniwal, 2013;Petrović et al, 2010;Ishikawa et al, 2012;Mathioudakis and Koudas, 2010;Long et al, 2011;Rosa et al, 2011;Popescu and Pennacchiotti, 2010;Benson et al, 2011;Lee and Sumiya, 2010;Mehrotra et al, 2013;Rajani et al, 2014;Weng et al, 2010;Zhao et al, 2011;Diao et al, 2012;Dong et al, 2015;Fang et al, 2014;Cataldi et al, 2010;Cordeiro, 2012;Kwan et al, 2013;Weiler et al, 2014;Aggarwal and Subbian, 2012;Tembhurnikar and Patil, 2015;Katragadda et al, 2017;Manaskasemsak et al, 2016;Zhang et al, 2015;Huang et al, 2013;Kaleel et al, 2013;Rafea an...…”

Section: Concepts Of Event Detectionmentioning

confidence: 99%

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A Survey on Event Detection Models for Text Data Streams

Al-Dyani¹,

Ahmad²,

Kamaruddin³

2020

Journal of Computer Science

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Event Detection (ED) is a study area that attracts the attention of decision-makers from various disciplines in order to help them in taking the right decision. ED has been examined on various text streams like Twitter, Facebook, Emails, Blogs, Web Forums and newswires. Many ED models have been proposed in literature. In general, ED model consists of six main phases: Data collection, pre-processing, feature selection, event detection, performance evaluation and result representation. Among these phases, event detection phase has a vital rule in the performance of the ED model. Consequently, numerous supervised, unsupervised, semi-supervised detection methods have been introduced for this phase. However, unsupervised methods have been extensively utilized as ED process is considered as unsupervised task. Hence, such methods need to be categorized on such a way so it can help researchers to understand and identified the limitations lay in these methods. In this survey, ED models for text data from various Social Network sites (SNs) are analyzed based on domain type, detection methods, type of detection task. In addition, main categories for unsupervised detection methods are explicitly mentioned with revising their related works. Moreover, the major open challenges faced by researchers for building ED models are explained and discussed in detail. The main objective of this survey paper is to provide a complete view of the recent developments in ED field. Hence, help scholars to identify the limitations of existing ED models for text data and help them to recognize the interesting future works directions.

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Section: Clustering-based Methodsmentioning

confidence: 99%

Section: Graph-based Clustering Methodsmentioning

confidence: 99%

Section: Concepts Of Event Detectionmentioning

confidence: 99%

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A Survey on Event Detection Models for Text Data Streams

Al-Dyani¹,

Ahmad²,

Kamaruddin³

2020

Journal of Computer Science

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“…Additionally, we believe that the relationship between social media users who discuss the same topics also plays a key role in topic relevance. Kwan et al [9] proposed a measure referred to as reciprocity, which attempts to detect the interaction between social media users and perceive their engagement in relation to a particular topic. Higher reciprocity means greater interaction between users, and thus topics with higher reciprocity should be considered more important because of their underlying community structure.…”

Section: Social Network Analysismentioning

confidence: 99%

BlogNewsRank: Finding and Ranking Frequent News Topics Using Social Media Factors

Harshitha

Rafi

2018

JOIV : Int. J. Inform. Visualization

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In early days, mass media sources such as news media used to inform us about daily events. Now a days, social media services such as Twitter huge amount of user-generated data, which has a great potential to contain informative news-related content. For these resources to be useful, we have to find a way to filter noise and capture the content that, based on its similarity to the news media, is considered valuable. Even after noise is removed, information overload may still exist in the remaining data. Hence it is convenient to prioritize it for consumption. To achieve prioritization, information must be ranked in order of estimated importance considering mainly three factors. First, the temporal prevalence of a particular topic in the news media is a factor of importance, and can be considered the media focus (MF) of a topic. Second, the temporal prevalence of the topic in social media indicates its user attention (UA). Last, the interaction between the social media users who mention this topic indicates the strength of the community discussing it, and can be regarded as the user interaction (UI) toward the topic. We propose an unsupervised framework—BlogNewsRank—which identiﬁes news topics prevalent in both social media and the news media, and then ranks them by relevance(frequency) using their degrees of MF, UA, and UI.

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“…It has also been applied to detect events from folksonomies in some works [7,43,42]. A model called "keyword-based evolving graph sequences" (kEGS) is proposed to capture the characteristics of information propagation in social streams [20], which also identify events based on word frequency. However, most of these works cannot well exploit the social knowledge of micro-blog data.…”

Section: Related Workmentioning

confidence: 99%