Content-driven trust propagation framework

Vydiswaran, V. G. Vinod; Zhai, ChengXiang; Roth, Dan

doi:10.1145/2020408.2020567

Cited by 49 publications

(39 citation statements)

References 23 publications

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“…Such an approach has been found to be quite effective for news analysis [23] and sentiment analysis [13,21] tasks. However, sometimes use of negative words like "not" can flip the semantics even though large number of words are shared.…”

Section: Tweet Implicationsmentioning

confidence: 99%

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Evaluating Event Credibility on Twitter

Gupta

Zhao

Han³

2012

Proceedings of the 2012 SIAM International Conference on Data Mining

222

156

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Though Twitter acts as a realtime news source with people acting as sensors and sending event updates from all over the world, rumors spread via Twitter have been noted to cause considerable damage. Given a set of popular Twitter events along with related users and tweets, we study the problem of automatically assessing the credibility of such events.We propose a credibility analysis approach enhanced with event graph-based optimization to solve the problem. First we experiment by performing PageRanklike credibility propagation on a multi-typed network consisting of events, tweets, and users. Further, within each iteration, we enhance the basic trust analysis by updating event credibility scores using regularization on a new graph of events.Our experiments using events extracted from two tweet feed datasets, each with millions of tweets show that our event graph optimization approach outperforms the basic credibility analysis approach. Also, our methods are significantly more accurate (∼86%) than the decision tree classifier approach (∼72%).

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Section: Tweet Implicationsmentioning

confidence: 99%

“…This work was followed by some more fact finder algorithms: Sums, Average.Log, Investment, Pooled Investment by Pasternack et al [14]. A large body of work [2,3,8,23,26] has been done further, in this area.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Event Credibility on Twitter

Gupta

Zhao

Han³

2012

Proceedings of the 2012 SIAM International Conference on Data Mining

222

156

View full text Add to dashboard Cite

show abstract

“…If true, the process diverges into two branches: i) adding the value to the existing group (lines [11][12][13][14] and ii) setting up a new group for the value (lines [15][16][17][18]. In the first case, Algorithm 2 invokes itself, taking the updated solution (with the new value added to the last group) and the updated sublist (with the first value removed) as inputs (line 12).…”

Section: Content-based Groupingmentioning

confidence: 99%

“…Other related approaches include crowdsourcing [9] and web search [2,17]. Crowdsourcing systems assess worker quality in accomplishing their crowdsourcing tasks while web search methods recommend trustworthy source links to answer user's queries.…”

Section: Related Workmentioning

confidence: 99%

Approximate Truth Discovery via Problem Scale Reduction

Wang

Sheng

Fang

et al. 2015

Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

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Many real-world applications rely on multiple data sources to provide information on their interested items. Due to the noises and uncertainty in data, given a specific item, the information from different sources may conflict. To make reliable decisions based on these data, it is important to identify the trustworthy information by resolving these conflicts, i.e., the truth discovery problem. Current solutions to this problem detect the veracity of each value jointly with the reliability of each source for every data item. In this way, the efficiency of truth discovery is strictly confined by the problem scale, which in turn limits truth discovery algorithms from being applicable on a large scale. To address this issue, we propose an approximate truth discovery approach, which divides sources and values into groups according to a userspecified approximation criterion. The groups are then used for efficient inter-value influence computation to improve the accuracy. Our approach is applicable to most existing truth discovery algorithms. Experiments on real-world datasets show that our approach improves the efficiency compared to existing algorithms while achieving similar or even better accuracy. The scalability is further demonstrated by experiments on large synthetic datasets.

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“…Being able to extract potential facts from text and judge their veracity is not the goal of this work, but has been explored elsewhere (e.g. [20]). …”

Section: Notion Of Medical Reliabilitymentioning

confidence: 99%

Reliability Prediction of Webpages in the Medical Domain

Sondhi

Vydiswaran

Zhai

2012

Lecture Notes in Computer Science

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Abstract. In this paper, we study how to automatically predict reliability of web pages in the medical domain. Assessing reliability of online medical information is especially critical as it may potentially influence vulnerable patients seeking help online. Unfortunately, there are no automated systems currently available that can classify a medical webpage as being reliable, while manual assessment cannot scale up to process the large number of medical pages on the Web. We propose a supervised learning approach to automatically predict reliability of medical webpages. We developed a gold standard dataset using the standard reliability criteria defined by the Health on Net Foundation and systematically experimented with different link and content based feature sets. Our experiments show promising results with prediction accuracies of over 80%. We also show that our proposed prediction method is useful in applications such as reliability-based re-ranking and automatic website accreditation.

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Content-driven trust propagation framework

Cited by 49 publications

References 23 publications

Evaluating Event Credibility on Twitter

Evaluating Event Credibility on Twitter

Approximate Truth Discovery via Problem Scale Reduction

Reliability Prediction of Webpages in the Medical Domain

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