Mining Newsworthy Topics from Social Media

Martin, Carlos; Corney, David; Göker, Ayşe

doi:10.1007/978-3-319-18458-6_2

Cited by 10 publications

(9 citation statements)

References 22 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A follow-up work, presented by Martin et al [39], includes a similar evaluation. Additionally, they attempt to derive the best slot size for the BNgram technique as well as the best combination of clustering and topic ranking techniques.…”

Section: Evaluation Of Event Detection Approachesmentioning

confidence: 95%

Editorial: Survey and Experimental Analysis of Event Detection Techniques for Twitter

Weiler

Grossniklaus

Scholl

2016

The Computer Journal

View full text Add to dashboard Cite

Twitter's popularity as a source of up-to-date news and information is constantly increasing. In response to this trend, numerous event detection techniques have been proposed to cope with the rate and volume of Twitter data streams. Although most of these works conduct some evaluation of the proposed technique, a comparative study is often omitted. In this paper, we present a survey and experimental analysis of state-of-the-art event detection techniques for Twitter data streams. In order to conduct this study, we define a series of measures to support the quantitative and qualitative comparison. We demonstrate the effectiveness of these measures by applying them to event detection techniques as well as to baseline approaches using real-world Twitter streaming data.

show abstract

Section: Evaluation Of Event Detection Approachesmentioning

confidence: 95%

Editorial: Survey and Experimental Analysis of Event Detection Techniques for Twitter

Weiler

Grossniklaus

Scholl

2016

The Computer Journal

View full text Add to dashboard Cite

show abstract

“…Applications include detection of events in sports [7,28,42], public emergencies and disease outbreaks [3,34] and natural disasters [8,33]. Several recent works perform general event detection and trend spotting on Twitter by detecting anomalous or "bursty" phrases [6,23,36]. However, Twitter presents many challenges due to the short, unstructured, usually poorly formatted messages that users post, and lacks explicit community structure corresponding to user membership or content relevancy.…”

Section: Event Detection In Social Mediamentioning

confidence: 99%

Unsupervised Content-Based Characterization and Anomaly Detection of Online Community Dynamics

Shah

Hurley

Liu³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

The structure and behavior of human networks have been investigated and quantitatively modeled by modern social scientists for decades, however the scope of these efforts is often constrained by the labor-intensive curation processes that are required to collect, organize, and analyze network data. The surge in online social media in recent years provides a new source of dynamic, semi-structured data of digital human networks, many of which embody attributes of real-world networks. In this paper we leverage the Reddit social media platform to study social communities whose dynamics indicate they may have experienced a disturbance event. We describe an unsupervised approach to analyzing natural language content for quantifying community similarity, monitoring temporal changes, and detecting anomalies indicative of disturbance events. We demonstrate how this method is able to detect anomalies in a spectrum of Reddit communities and discuss its applicability to unsupervised event detection for a broader class of social media use cases. 1 This distinction is subtle. In the context of this work we assume a 1:1 mapping between the (observable) network and the corresponding (sub)community it represents, and use these terms interchangeably.

show abstract

“…A news search engine for interesting topics is described in [10]. The information is obtained from an information flow, and it is organized in a way that the user does not feel overwhelmed.…”

Section: Related Workmentioning

confidence: 99%

About the effects of sentiments on topic detection in social networks

Gutiérrez-Batista¹,

Campaña²,

Martinez-Folgoso³

et al. 2016

Int. J. DNE

View full text Add to dashboard Cite

Topic detection from large textual data volumes extracted from Social Networks is an interesting research topic in the context of Big Data. The textual content present in Social Networks contains diverse information that can be exploited in order to obtain useful information. Topic detection and sentiment analysis in social networks are topics of widespread research. The study of both is sometimes intertwined as, usually, user messages revolve around a particular topic and express certain attitude of the user towards the topic discussed. However, this assumption is not valid for all messages as some of them express only general feelings or attitudes and do not refer to something in particular that covers up the topic discussed. In fact, these messages can influence the topic detection process. In this paper, we propose to obtain topics from massive quantities of text data extracted from social networks, without using previous information, and only with the use of unsupervised data mining techniques. We analyze the influence of sentiments in messages and how they affect the topic detection task. Terms related to sentiments provide useful information for a variety of applications, but not for topic detection where they represent a source of unnecessary noise. Experiments are conducted on data obtained from Twitter social network.

show abstract

Mining Newsworthy Topics from Social Media

Cited by 10 publications

References 22 publications

Editorial: Survey and Experimental Analysis of Event Detection Techniques for Twitter

Editorial: Survey and Experimental Analysis of Event Detection Techniques for Twitter

Unsupervised Content-Based Characterization and Anomaly Detection of Online Community Dynamics

About the effects of sentiments on topic detection in social networks

Contact Info

Product

Resources

About