Topic and Opinion Classification Based Information Credibility Analysis on Twitter

Ikegami, Yukino; Kawai, Kenta; Namihira, Yoshimi; Tsuruta, Setsuo

doi:10.1109/smc.2013.796

Cited by 21 publications

(9 citation statements)

References 10 publications

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“…In addition, the number of verbs and nouns used to describe an event are considered. Some authors have measured credibility at this level using topic and opinion classification [68], [69]. This method assumes that, to assess the information credibility of an event, one must account for the different opinions [72] of Twitter's many users worldwide.…”

Section: B Topic Levelmentioning

confidence: 99%

Credibility in Online Social Networks: A Survey

et al. 2019

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The importance of information credibility in society cannot be underestimated given that it is at the heart of all decision-making. Generally, more information is better; however, knowing the value of this information is essential for the decision-making processes. Information credibility defines a measure of the fitness of the information for consumption. It can also be defined in terms of reliability, which denotes the probability that a data source will appear credible to the users. A challenge in this topic is that there is a great deal of literature that has developed different credibility dimensions. In addition, information science dealing with online social networks has grown in complexity, attracting interest from researchers in information science, psychology, human-computer interaction, communication studies, and management studies, all of whom have studied the topic from different perspectives. This work will attempt to provide an overall review of the credibility assessment literature over the period 2006-2017 as applied to the context of the microblogging platform, Twitter. The known interpretations of credibility will be examined, particularly as they relate to the Twitter environment. In addition, we investigate levels of credibility assessment features. We then discuss recent works, addressing a new taxonomy of credibility analysis and assessment techniques. At last, a cross-referencing of literature is performed while suggesting new topics for future studies of credibility assessment in a social media context.

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Section: B Topic Levelmentioning

confidence: 99%

Credibility in Online Social Networks: A Survey

et al. 2019

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“…The results showed that the social model outperformed the others in terms of predictive accuracy. Ikegami et al [5] proposed a model to do credibility analysis of Twitter tweets based on topic and opinion classification. Latent Dirichlet Allocation (LDA) is used to identify the topics.…”

Section: Related Workmentioning

confidence: 99%

“…) = Set of nodes of all the neighbors of v λ=constantCloseness centralityCloseness Centrality refers to how close to others a node is.The closeness centrality is calculated by(5) Where d (y, x) is distance between x and y…”

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confidence: 99%

Classification based Credibility Analysis on Twitter Data

R*¹,

Aravind²

2019

IJRTE

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Twitter is an important place to get access to breaking news and information. So, it is necessary to check the trustworthiness of tweets. Credibility is used to assess the quality of being believable or worthy of trust. Credibility analysis refers to attempt to an ascertain truthfulness in short lie detection. In this work, credibility of the twitter data can be assessed using centrality measures. First the tweets are preprocessed using the preprocessing techniques. The preprocessing techniques on tweets: a stop word removal, stemming, pos tagging etc. are used to improve the performance. Preprocessed twitter data can be used to identify the tweet and author features. Then the centrality measures are applied to the preprocessed dataset. The proposed centrality measures used in this work are Betweeness centrality, Eigenvector centrality, Degree centrality and Closeness centrality. The centrality measures are used to find out the trust between the users and it will be given as input to classifiers. The classifiers like Naïve Bayesian, Support Vector Machine and K Nearest Neighbor are used to classify the tweets based on credibility.

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“…In total, we gather features from 17 different papers, some of which we have already discussed. We gather features from works that use classifiers to automatically predict credibility [8,50,21,28,49,9,16,15,7], features from works that use learning to rank algorithms [18,17], and features gleaned from works that take hybrid or other appraoches to quantify and model credibility [40,24,45,1,42,3]. In Table 2.…”

Section: Popular Features From Previous Workmentioning

confidence: 99%

Features for Ranking Tweets Based on Credibility and Newsworthiness

Ross

Thirunarayan

2016

2016 International Conference on Collaboration Technologies and Systems (CTS)

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We create a robust and general feature set for learning to rank algorithms that rank tweets based on credibility and newsworthiness. In previous works, it has been demonstrated that when the training and testing data are from two distinct time periods, the ranker performs poorly. We improve upon previous work by creating a feature set that does not over fit a particular year or set of topics. This is critical given how people utilize social media changes as time progresses, and the topics discussed vary. In addition, we are constantly gaining new tweet data. Thus, it is important to be able to have a set of features that can perform well across many different topics, and across different years. In our approach, we present a methodology for selecting features based on how they can capture credibility and newsworthiness regardless of year and topic. In order to derive such features, we use the studies done on credibility perception of social media as well as the clues provided in past works in this domain. We also present new features that, to our knowledge, have not been used in previous works in this domain.iii

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Topic and Opinion Classification Based Information Credibility Analysis on Twitter

Cited by 21 publications

References 10 publications

Credibility in Online Social Networks: A Survey

Credibility in Online Social Networks: A Survey

Classification based Credibility Analysis on Twitter Data

Features for Ranking Tweets Based on Credibility and Newsworthiness

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