Provenance-Based Rumor Detection

Duong, Chi Thang; Nguyen, Quoc Viet Hung; Wang, Sen; Stantić, Bela

doi:10.1007/978-3-319-68155-9_10

Cited by 20 publications

(13 citation statements)

References 14 publications

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“…While there is a large body of work on rumour detection on social platforms, surveyed in [56], little has been done to exploit multiple modalities to detect rumours. Most work leverages only textual data such as tweets [13,55,21]; whereas others consider different data entities such as users and hashtags but still treat them as additional features or textual data only [30,19]. Techniques based on hand-crafted features [13,55,50] are grounded in an ad-hoc definition of features, which are expected to be strong indicators of rumours.…”

Section: Related Workmentioning

confidence: 99%

From anomaly detection to rumour detection using data streams of social platforms

et al. 2019

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Social platforms became a major source of rumours. While rumours can have severe real-world implications, their detection is notoriously hard: Content on social platforms is short and lacks semantics; it spreads quickly through a dynamically evolving network; and without considering the context of content, it may be impossible to arrive at a truthful interpretation. Traditional approaches to rumour detection, however, exploit solely a single content modality, e.g., social media posts, which limits their detection accuracy. In this paper, we cope with the aforementioned challenges by means of a multi-modal approach to rumour detection that identifies anomalies in both, the entities (e.g., users, posts, and hashtags) of a social platform and their relations. Based on local anomalies, we show how to detect rumours at the network level, following a graph-based scan approach. In addition, we propose incremental methods, which enable us to detect rumours using streaming data of social platforms. We illustrate the effectiveness and efficiency of our approach with a real-world dataset of 4M tweets with more than 1000 rumours.

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Section: Related Workmentioning

confidence: 99%

From anomaly detection to rumour detection using data streams of social platforms

et al. 2019

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“…Again, posterior knowledge on user input cannot be incorporated. Also, approaches based on gradientdescent [47,20,24,55] only optimise model parameters, but neglect external probability constraints. Fact extraction may be performed by diverse data representations, e.g., knowledge bases [22], web tables [17], semi-structured data [26,62], or free text [15].…”

Section: Related Workmentioning

confidence: 99%

User guidance for efficient fact checking

et al. 2019

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The Web constitutes a valuable source of information. In recent years, it fostered the construction of large-scale knowledge bases, such as Freebase, YAGO, and DBpedia. The open nature of the Web, with content potentially being generated by everyone, however, leads to inaccuracies and misinformation. Construction and maintenance of a knowledge base thus has to rely on fact checking, an assessment of the credibility of facts. Due to an inherent lack of ground truth information, such fact checking cannot be done in a purely automated manner, but requires human involvement. In this paper, we propose a comprehensive framework to guide users in the validation of facts, striving for a minimisation of the invested effort. Our framework is grounded in a novel probabilistic model that combines user input with automated credibility inference. Based thereon, we show how to guide users in fact checking by identifying the facts for which validation is most beneficial. Moreover, our framework includes techniques to reduce the manual effort invested in fact checking by determining when to stop the validation and by supporting efficient batching strategies. We further show how to handle fact checking in a streaming setting. Our experiments with three real-world datasets demonstrate the efficiency and effectiveness of our framework: A knowledge base of high quality, with a precision of above 90%, is constructed with only a half of the validation effort required by baseline techniques.

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“…We use the synthetic dataset, setting b = 20 following studies on human cognitive load [8,10,18,19,42,50]. The observed runtimes, when varying the length of the input sequence, are shown in Fig.…”

Section: B Evaluating the Efficiencymentioning

confidence: 99%

What-If Analysis with Conflicting Goals: Recommending Data Ranges for Exploration

Nguyen¹,

Zheng

Weidlich

et al. 2018

2018 IEEE 34th International Conference on Data Engineering (ICDE)

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What-if analysis is a data-intensive exploration to inspect how changes in a set of input parameters of a model influence some outcomes. It is motivated by a user trying to understand the sensitivity of a model to a certain parameter in order to reach a set of goals that are defined over the outcomes. To avoid an exploration of all possible combinations of parameter values, efficient what-if analysis calls for a partitioning of parameter values into data ranges and a unified representation of the obtained outcomes per range. Traditional techniques to capture data ranges, such as histograms, are limited to one outcome dimension. Yet, in practice, what-if analysis often involves conflicting goals that are defined over different dimensions of the outcome. Working on each of those goals independently cannot capture the inherent trade-off between them. In this paper, we propose techniques to recommend data ranges for what-if analysis, which capture not only data regularities, but also the trade-off between conflicting goals. Specifically, we formulate a parametric data partitioning problem and propose a method to find an optimal solution for it. Targeting scalability to large datasets, we further provide a heuristic solution to this problem. By theoretical and empirical analyses, we establish performance guarantees in terms of runtime and result quality.

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Provenance-Based Rumor Detection

Cited by 20 publications

References 14 publications

From anomaly detection to rumour detection using data streams of social platforms

From anomaly detection to rumour detection using data streams of social platforms

User guidance for efficient fact checking

What-If Analysis with Conflicting Goals: Recommending Data Ranges for Exploration

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