Characterizing Online Rumoring Behavior Using Multi-Dimensional Signatures

Maddock, Jim; Starbird, Kate; Al-Hassani, Haneen J.; Sandoval, Daniel E.; Orand, Mania; Mason, Robert M.

doi:10.1145/2675133.2675280

Cited by 88 publications

(76 citation statements)

References 15 publications

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“…Name and entity recognition (NER) has been widely studied in computational linguistics, and well known solutions have been developed, such as StanfordNER 9 and OpenNLP 10 . Traditional NER solutions, however, focus only on pre-defined term categories, such as person, organization, and location [12,3].…”

Section: Related Workmentioning

confidence: 99%

“…Existing work in this field usually requires selecting small portions of data from a large, mixed-domain data body. For example, as a service such as Twitter allows public access to all its data 6 , certain portions of this data have been collected for applications in narrow domains, including earthquake monitoring [15], influenza surveillance [2], election result prediction [18,19], ideal point estimation [1], and rumor detection [5,9].…”

Section: Introductionmentioning

confidence: 99%

“…In [19] tweets related to two candidates are collected. In [9], only tweets related to the bombing incident are selected. The techniques for such filtering range from machine learning based approaches [15,13], to keyword generation [11] and clustering [20].…”

Section: Introductionmentioning

confidence: 99%

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Identifying Domains and Concepts in Short Texts via Partial Taxonomy and Unlabeled Data

Zhang

Szabo

Sheng

et al. 2017

Advanced Information Systems Engineering

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Abstract. Accurate and real-time identification of domains and concepts discussed in microblogging texts is crucial for many important applications such as earthquake monitoring, influenza surveillance and disaster management. Existing techniques such as machine learning and keyword generation are application specific and require significant amount of training in order to achieve high accuracy. In this paper, we propose to use a multiple domain taxonomy (MDT) to capture general user knowledge. We formally define the problems of domain classification and concept tagging. Using the MDT, we devise domain-independent pure frequency count methods that do not require any training data nor annotations and that are not sensitive to misspellings or shortened word forms. Our extensive experimental analysis on real Twitter data shows that both methods have significantly better identification accuracy with low runtime than existing methods for large datasets.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identifying Domains and Concepts in Short Texts via Partial Taxonomy and Unlabeled Data

Zhang

Szabo

Sheng

et al. 2017

Advanced Information Systems Engineering

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“…Snopes.com), classical machine learning frameworks [20,34,38] to deep learning models [29,39,56,57] were developed to determine credibility of online news and information. However, falsified news is still disseminated like wild fire [31,59] despite dramatic rise of fact-checking sites worldwide [21]. Furthermore, recent work showed that individuals tend to selectively consume news that have ideologies similar to what they believe while disregarding contradicting arguments [8,35].…”

Section: Introductionmentioning

confidence: 99%

Learning from Fact-checkers

Lee

2019

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

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In fighting against fake news, many fact-checking systems comprised of human-based fact-checking sites (e.g., snopes.com and politifact.com) and automatic detection systems have been developed in recent years. However, online users still keep sharing fake news even when it has been debunked. It means that early fake news detection may be insufficient and we need another complementary approach to mitigate the spread of misinformation. In this paper, we introduce a novel application of text generation for combating fake news. In particular, we (1) leverage online users named fact-checkers, who cite fact-checking sites as credible evidences to fact-check information in public discourse; (2) analyze linguistic characteristics of fact-checking tweets; and (3) propose and build a deep learning framework to generate responses with factchecking intention to increase the fact-checkers' engagement in fact-checking activities. Our analysis reveals that the fact-checkers tend to refute misinformation and use formal language (e.g. few swear words and Internet slangs). Our framework successfully generates relevant responses, and outperforms competing models by achieving up to 30% improvements. Our qualitative study also confirms that the superiority of our generated responses compared with responses generated from the existing models.

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“…Because social media data is large and complex, studying it requires laborious human judgment, consideration of context, and nuanced slicing and cleaning; many of the new methods being explored combine qualitative and quantitative techniques [10,16]. Regardless of the specific approach taken, the complexity and diversity of social media datasets warrant that care be taken in exploratory data analysis.…”

Section: Introductionmentioning

confidence: 99%