Cluster-discovery of Twitter messages for event detection and trending

Kaleel, Shakira Banu; Abhari, Abdolreza

doi:10.1016/j.jocs.2014.11.004

Cited by 67 publications

(32 citation statements)

References 26 publications

(50 reference statements)

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“…The most adopted algorithms to obtain algorithms, such algorithms discover sentences which belong to different events [34,35,36]. To ensure the data is valid, we apply the state-of-the-art event discovery methods to reduce the possible negative effects on core semantics discovery [37,38,39,40]. Besides, association relation based representation in our model can further reduces these adverse effects caused by noise and irrelevant short texts.…”

Section: Proposed Methodsmentioning

confidence: 99%

Discovering the core semantics of event from social media

Liu

Luo

Gong³

et al. 2016

Future Generation Computer Systems

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As social media is opening up such as Twitter and Sina Weibo 1 , large volumes of short texts are flooding on the Web. The ocean of short texts dilutes the limited core semantics of event in cyberspace by redundancy, noises and irrelevant content on the web, which make it difficult to discover the core semantics of event. The major challenges include how to efficiently learn the semantic association distribution by small-scale association relations and how to maximize the coverage of the semantic association distribution by the minimum number of redundancy-free short texts. To solve the above issues, we explore a Markov random field based method for discovering the core semantics of event. This method makes semantics collaborative computation for learning association relation distribution and makes information gradient computation for discovering k redundancy-free texts as the core semantics of event. We evaluate our method by comparing with two state-of-the-art methods on the TAC dataset and the microblog dataset. The results show our method outperforms other methods in

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Section: Proposed Methodsmentioning

confidence: 99%

Discovering the core semantics of event from social media

Liu

Luo

Gong³

et al. 2016

Future Generation Computer Systems

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“…Additionally, some works calculate the F 1 score, average precision, or the area under the receiver-operating curve. While such measures that evaluate the task-based performance of a technique are quite common, only six works ( [3], [13], [22], [25], [39], [41]) apply a measure to evaluate the run-time performance. Apart from these well-known measures, some novel measures were defined.…”

Section: B Evaluation Issuesmentioning

confidence: 99%

Towards Reproducible Research of Event Detection Techniques for Twitter

Weiler

Schilling

Kircher

et al. 2019

2019 6th Swiss Conference on Data Science (SDS)

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A major challenge in many research areas is reproducibility of implementations, experiments, or evaluations. New data sources and research directions complicate the reproducibility even more. For example, Twitter continues to gain popularity as a source of up-to-date news and information. As a result, numerous event detection techniques have been proposed to cope with the steadily increasing rate and volume of social media data streams. Although some of these works provide their implementation or conduct an evaluation of the proposed technique, it is almost impossible to reproduce their experiments. The main drawback is that Twitter prohibits the release of crawled datasets that are used by researchers in their experiments. In this work, we present a survey of the vast landscape of implementations, experiments, and evaluations presented by the different research works. Furthermore, we propose a reproducibility toolkit including Twistor (Twitter Stream Simulator), which can be used to simulate an artificial Twitter data stream (including events) as input for the experiments or evaluations of event detection techniques. We further present the experimental application of the reproducibility toolkit to stateof-the-art event detection techniques.

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“…Term-pivot techniques work on n-grams features, aiming to detect representative terms for the event in question. Many data mining techniques have been used in these two approaches, including hierarchical clustering techniques based on pairwise distances [21], wavelet analysis of word frequencies to obtain features for each word [22], and locality sensitive hashing (LSH) to discover potential events [23]. For coordinate-based detection, spatial proximity has been widely used to prepare candidate tweets for local events [3,24].…”

Section: Related Workmentioning

confidence: 99%

Tracing the Spatial-Temporal Evolution of Events Based on Social Media Data

Zhou

Chen

2017

IJGI

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Social media data provide a great opportunity to investigate event flow in cities. Despite the advantages of social media data in these investigations, the data heterogeneity and big data size pose challenges to researchers seeking to identify useful information about events from the raw data. In addition, few studies have used social media posts to capture how events develop in space and time. This paper demonstrates an efficient approach based on machine learning and geovisualization to identify events and trace the development of these events in real-time. We conducted an empirical study to delineate the temporal and spatial evolution of a natural event (heavy precipitation) and a social event (Pope Francis' visit to the US) in the New York City-Washington, DC regions. By investigating multiple features of Twitter data (message, author, time, and geographic location information), this paper demonstrates how voluntary local knowledge from tweets can be used to depict city dynamics, discover spatiotemporal characteristics of events, and convey real-time information.

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Cluster-discovery of Twitter messages for event detection and trending

Cited by 67 publications

References 26 publications

Discovering the core semantics of event from social media

Discovering the core semantics of event from social media

Towards Reproducible Research of Event Detection Techniques for Twitter

Tracing the Spatial-Temporal Evolution of Events Based on Social Media Data

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