BursT: A Dynamic Term Weighting Scheme for Mining Microblogging Messages

Lee, Chung-Hong; Wu, Chih-Hong; Chien, Tzan-Feng

doi:10.1007/978-3-642-21111-9_62

Cited by 29 publications

(14 citation statements)

References 5 publications

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“…The approach BursT by Lee et al [11] scores words dynamically based on frequency statistics collected during a time window. The main disadvantage of BursT is that it assigns high scores to regularly mentioned (familiar) words, e.g., "job", "evening", and "lol".…”

Section: Related Workmentioning

confidence: 99%

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On the locality of keywords in Twitter streams

Abdelhaq

Gertz

2014

Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming

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The continuously increasing popularity of social media sites such as Twitter and Facebook has recently led to a number of approaches to detect and extract event information from social media streams. Such events play an important role, e.g., in supporting location-based services and improving situational awareness. Moreover, the introduction of GPSequipped communication devises has led to an increase in the percentage of geo-tagged messages. These help to detect localized events, i.e., events occurring at a certain location, such as sport events or accidents. The main entities that indicate a localized event are local keywords that exhibit a surge in usage at the event location.In this paper, we propose an approach to extract local keywords from a Twitter stream by (1) identifying local keywords, and (2) estimating the central location of each keyword. This extraction process is performed in an online fashion using a sliding window on the Twitter stream. In addition, we address the problem of spatial outliers that adversely affect a proper identification of local keywords. Outliers occur when people far away from an event location use related keywords in their Tweets. We handle this problem by adjusting the spatial distribution of keywords based on their co-occurrence with place names that may refer to the location of an event. We evaluate the performance of our framework to reliably and efficiently extracting local keywords and estimating their central locations using a Twitter dataset.

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Section: Related Workmentioning

confidence: 99%

“…In stage-1, they apply BursT [11] to dynamically assign a score to each word. Then, IncrementalDBSCAN is employed to cluster emerging topics in real-time.…”

Section: Related Workmentioning

confidence: 99%

On the locality of keywords in Twitter streams

Abdelhaq

Gertz

2014

Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming

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“…Another approach in twitter trend analysis is bursty topic detection, which are trendier in the time series. Lee et al [38] developed a sliding window model and it considers the time factor of the term frequency. It calculates the term weights based on the arrival rates in a specified time frame.…”

Section: Literature Surveymentioning

confidence: 99%

Extracting Actionable Knowledge from Domestic Violence Discourses on Social Media

Subramani¹,

O’Connor²

2018

ICST Transactions on Scalable Information Systems

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Domestic Violence (DV) is considered as big social issue and there exists a strong relationship between DV and health impacts of the public. Existing research studies have focused on social media to track and analyse real world events like emerging trends, natural disasters, user sentiment analysis, political opinions, and health care. However there is less attention given on social welfare issues like DV and its impact on public health. Recently, the victims of DV turned to social media platforms to express their feelings in the form of posts and seek the social and emotional support, for sympathetic encouragement, to show compassion and empathy among public. But, it is difficult to mine the actionable knowledge from large conversational datasets from social media due to the characteristics of high dimensions, short, noisy, huge volume, high velocity, and so on. Hence, this paper will propose a novel framework to model and discover the various themes related to DV from the public domain. The proposed framework would possibly provide unprecedentedly valuable information to the public health researchers, national family health organizations, government and public with data enrichment and consolidation to improve the social welfare of the community. Thus provides actionable knowledge by monitoring and analysing continuous and rich user generated content.

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“…H t (w) = −(P t (w) log 2 (P t (w))) (4 [86]. In some sense, this metric could combine the average difference feature and regression scores described above and is therefore included here as well.…”

Section: Entropymentioning

confidence: 99%

Discovering Credible Events in Near Real Time from Social Media Streams

Buntain

2015

Proceedings of the 24th International Conference on World Wide Web

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Recent reliance on social media platforms as major sources of news and information, both for journalists and the larger population and especially during times of crisis, motivate the need for better methods of identifying and tracking high-impact events in these social media streams. Social media's volume, velocity, and democratization of information (leading to limited quality controls) complicate rapid discovery of these events and one's ability to trust the content posted about these events. This dissertation addresses these complications in four stages, using Twitter as a model social platform. The first stage analyzes Twitter's response to major crises, specifically terrorist attacks in Western countries, showing these high-impact events do not significantly impact message or user volume. Instead, these events drive changes in Twitter's topic distribution, with conversation, retweets, and hashtags relevant to these events experiencing significant, rapid, and short-lived bursts in frequency.Furthermore, conversation participants tend to prefer information from local authorities/organizations/media over national or international sources, with accounts for local police or local newspapers often emerging as central in the networks of interaction. Building on these results, the second stage in this dissertation presents and evaluates a set of features that capture these topical bursts associated with crises by modeling bursts in frequency for individual tokens in the Twitter stream. The resulting streaming algorithm is capable of discovering notable moments across a series of major sports competitions using Twitter's public stream without relying on domain-or language-specific information or models. Furthermore, results demonstrate models trained on sporting competition data perform well when transferred to earthquake identification. This streaming algorithm is then extended in this dissertation's third stage to support real-time event tracking and summarization. This real-time algorithm leverages new distributed processing technology to operate at scale and is evaluated against a collection of other community-developed information retrieval systems, where it performs comparably. Further experiments also show this real-time burst detection algorithm can be integrated with these other information retrieval systems to increase overall performance. The final stage then investigates automated methods for evaluating credibility in social media streams by leveraging two existing data sets. These two data sets measure different types of credibility (veracity versus perception), and results show veracity is negatively correlated with the amount of disagreement in and length of a conversation, and perceptions of credibility are influenced by the amount of links to other pages, shared media about the event, and the number of verified users participating in the discussion. Contributions made across these four stages are then usable in the relatively new fields of computational journalism and crisis informatics, which seek ...

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BursT: A Dynamic Term Weighting Scheme for Mining Microblogging Messages

Cited by 29 publications

References 5 publications

On the locality of keywords in Twitter streams

On the locality of keywords in Twitter streams

Extracting Actionable Knowledge from Domestic Violence Discourses on Social Media

Discovering Credible Events in Near Real Time from Social Media Streams

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