Real time discussion retrieval from twitter

Milajevs, Dmitrijs; Bouma, Gosse

doi:10.1145/2487788.2488050

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…The connections between the topics in LDA and PLDA topic sets are established by aggJSD with Hungarian method, where the matched topics with less than 20% of divergence are selected for comparison. Moreover, a recent study on using LDA to classify tweets from a variety of datasets have shown promising results [35]. They provide evidence that our approach to discovering Twitter topics is robust and with expected higher precision and recall (lower-bounded by the findings in [35]).…”

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confidence: 67%

“…Moreover, a recent study on using LDA to classify tweets from a variety of datasets have shown promising results [35]. They provide evidence that our approach to discovering Twitter topics is robust and with expected higher precision and recall (lower-bounded by the findings in [35]). Next step is to formulate an emerging outbreak model that is built based on SIR model, which we will first briefly discuss.…”

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confidence: 67%

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Microblog Topic Contagiousness Measurement and Emerging Outbreak Monitoring

Chu

Wong

Chen

et al. 2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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A recent study on collective attention in Twitter shows that an epidemic spreading of hashtags is predominantly driven by external factors. We extend a time-series form of susceptible-infectious-recovered (SIR) model to monitor microblog emerging outbreaks by considering both endogenous and exogenous drivers. In addition, we adopt partially labeled Dirichlet allocation (PLDA) model to generate both background latent topics and hashtag topics. It overcomes the problem of small available samples in hashtag analysis by including related but unlabeled tweets through inference. We standardize hashtag topic contagiousness measure as the estimated effective-reproduction-number (R) derived from epidemiology. It is obtained by Bayesian parameter estimation. Guided byR, one can profile and categorize emerging topics, and generate alerts on potential outbreaks. Experiment results confirm the effectiveness of this approach.

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confidence: 67%

mentioning

confidence: 67%

Microblog Topic Contagiousness Measurement and Emerging Outbreak Monitoring

Chu

Wong

Chen

et al. 2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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“…There has been research looking into keyword expansion for these purposes. Improved collection of event-related tweets for increased coverage was pioneered by Milajevs et al [117]. They used Latent Dirichlet Allocation (LDA) to enable discovery of relevant keywords associated with a known event, which was designed for and tested with leisure events, which in their case included music and sporting events.…”

Section: Other Tasksmentioning

confidence: 99%

Mining social media for newsgathering: A review

Zubiaga

2019

Online Social Networks and Media

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Social media is becoming an increasingly important data source for learning about breaking news and for following the latest developments of ongoing news. This is in part possible thanks to the existence of mobile devices, which allows anyone with access to the Internet to post updates from anywhere, leading in turn to a growing presence of citizen journalism. Consequently, social media has become a go-to resource for journalists during the process of newsgathering. Use of social media for newsgathering is however challenging, and suitable tools are needed in order to facilitate access to useful information for reporting. In this paper, we provide an overview of research in data mining and natural language processing for mining social media for newsgathering. We discuss five different areas that researchers have worked on to mitigate the challenges inherent to social media newsgathering: news discovery, curation of news, validation and verification of content, newsgathering dashboards, and other tasks. We outline the progress made so far in the field, summarise the current challenges as well as discuss future directions in the use of computational journalism to assist with social media newsgathering. This review is relevant to computer scientists researching news in social media as well as for interdisciplinary researchers interested in the intersection of computer science and journalism.

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“…For Brexit, we downloaded a set of tweet ids 5 collected and released by Milajevs using the tool Poultry (Milajevs and Bouma 2013). The data set contained tweet ids related to Brexit from March 7 to August 24th.…”

Section: Twitter Data Setmentioning

confidence: 99%

Is Climate Change Controversial? Modeling Controversy as Contention Within Populations

Dori-Hacohen¹,

Jang²,

Allan³

2017

Preprint

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A growing body of research focuses on computationally detecting controversial topics and understanding the stances people hold on them. Yet gaps remain in our theoretical and practical understanding of how to define controversy, how it manifests, and how to measure it. In this paper, we introduce a novel measure we call "contention", defined with respect to a topic and a population. We model contention from a mathematical standpoint. We validate our model by examining a diverse set of sources: real-world polling data sets, actual voter data, and Twitter coverage on several topics. In our publiclyreleased Twitter data set of nearly 100M tweets, we examine several topics such as Brexit, the 2016 U.S. Elections, and "The Dress", and cross-reference them with other sources. We demonstrate that the contention measure holds explanatory power for a wide variety of observed phenomena, such as controversies over climate change and other topics that are well within scientific consensus. Finally, we re-examine the notion of controversy, and present a theoretical framework that defines it in terms of population. We present preliminary evidence suggesting that contention is one dimension of controversy, along with others, such as "importance". Our new contention measure, along with the hypothesized model of controversy, suggest several avenues for future work in this emerging interdisciplinary research area.

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Real time discussion retrieval from twitter

Cited by 8 publications

References 7 publications

Microblog Topic Contagiousness Measurement and Emerging Outbreak Monitoring

Microblog Topic Contagiousness Measurement and Emerging Outbreak Monitoring

Mining social media for newsgathering: A review

Is Climate Change Controversial? Modeling Controversy as Contention Within Populations

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