Given the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims. Here, we propose a general-purpose framework for fully-automatic fact checking using external sources, tapping the potential of the entire Web as a knowledge source to confirm or reject a claim. Our framework uses a deep neural network with LSTM text encoding to combine semantic kernels with task-specific embeddings that encode a claim together with pieces of potentially-relevant text fragments from the Web, taking the source reliability into account. The evaluation results show good performance on two different tasks and datasets: (i) rumor detection and (ii) fact checking of the answers to a question in community question answering forums.
Efforts to determine the credibility of an answer in order to assess its overall quality required the inclusion of content-based information @cite_9 , e.g., verbs and adjectives such as and , which cast doubt on the answer. Similarly, used source credibility (e.g., does the document come from a government Website?), sentiment analysis, and answer contradiction compared to other related answers.
@cite_9
2090380787 2085731449 2113770130 2553850906
Answer Validation is a topic of significant interest within the Question Answering community. In this paper, we propose the use of language modeling methodologies for Answer Validation, using corpus-based methods that do not require the use of external sources. Specifically, we propose a model for Answer Credibility which quantifies the reliability of a source document that contains a candidate answer and the Question's Context Model. Deciding whether a claim is true or false often requires a deeper understanding of the evidence supporting and contradicting the claim. However, when presented with many evidence documents, users do not necessarily read and trust them uniformly. Psychologists and other researchers have shown that users tend to follow and agree with articles and sources that hold viewpoints similar to their own, a phenomenon known as confirmation bias. This suggests that when learning about a controversial topic, human biases and viewpoints about the topic may affect what is considered "trustworthy" or credible. It is an interesting challenge to build systems that can help users overcome this bias and help them decide the truthfulness of claims. In this article, we study various factors that enable humans to acquire additional information about controversial claims in an unbiased fashion. Specifically, we designed a user study to understand how presenting evidence with contrasting viewpoints and source expertise ratings affect how users learn from the evidence documents. We find that users do not seek contrasting viewpoints by themselves, but explicitly presenting contrasting evidence helps them get a well-rounded understanding of the topic. Furthermore, explicit knowledge of the credibility of the sources and the context in which the source provides the evidence document not only affects what users read but also whether they perceive the document to be credible. Language: en Little of the work on online credibility assessment has considered how the information-seeking process figures into the final evaluation of content people encounter. Using unique data about how a diverse group of young adults looks for and evaluates Web content, our paper makes contributions to existing literature by highlighting factors beyond site features in how users assess credibility. We find that the process by which users arrive at a site is an important component of how they judge the final destination. In particular, search context, branding and routines, and a reliance on those in one’s networks play important roles in online information-seeking and evaluation. We also discuss that users differ considerably in their skills when it comes to judging online content credibility. The World Wide Web (WWW) has become a rapidly growing platform consisting of numerous sources which provide supporting or contradictory information about claims (e.g., "Chicken meat is healthy"). In order to decide whether a claim is true or false, one needs to analyze content of different sources of information on the Web, measure credibility of information sources, and aggregate all these information. This is a tedious process and the Web search engines address only part of the overall problem, viz., producing only a list of relevant sources. In this paper, we present ClaimEval, a novel and integrated approach which given a set of claims to validate, extracts a set of pro and con arguments from the Web information sources, and jointly estimates credibility of sources and correctness of claims. ClaimEval uses Probabilistic Soft Logic (PSL), resulting in a flexible and principled framework which makes it easy to state and incorporate different forms of prior-knowledge. Through extensive experiments on real-world datasets, we demonstrate ClaimEval's capability in determining validity of a set of claims, resulting in improved accuracy compared to state-of-the-art baselines.