Fully Automated Fact Checking Using External Sources

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.Comment: RANLP-201

Social networks : service selection and recommendation

University of Technology, Sydney. Faculty of Engineering and Information Technology.The Service-Oriented Computing paradigm is widely acknowledged for its potential to revolutionize the world of computing through the utilization of Web services. It is expected that Web services will fully leverage the Semantic Web to outsource some of their functionalities to other Web services that provide value-added services, and by integrating the business logic of Web services in the form of business to business and business to consumer e-commerce applications. In the Service Web, Web services and Web-Based Social Networks are emerging in which a wide range of similar functionalities are expected to be offered by a vast number of Web services, and applications can search and compose services according to users’ needs in a seamless and an automatic fashion. Web services are expected to outsource some of their functionalities to other Web services. In such situations, some services may be new to the service market, and some may act maliciously in order to be selected. A key requirement is to provide mechanisms for quality selection and recommendation of relevant Web services with perceived risk considerations. Although the future of Web service selection and recommendation looks promising, there are challenging issues related to user knowledge and behavior, as well as issues related to recommendation approaches. This dissertation addresses the demanding issues in Web service selection and recommendation from theory and practice perspectives. These challenges include cold-start users, who represent more than 50% of the social network population, the capture of users’ preferences, risk mitigation in service selection, customers’ privacy and application scalability. This dissertation proposes a novel approach to automate social-based Web service selection and recommendation in a dynamic environment. It utilizes Web-Based Social Networks and the “Follow the Leader” strategy, for a Credibility-based framework that includes two credibility models: the user Credibility model which is used to qualify consumers as either leaders or followers based on their credibility, and the service Credibility model which is used to identify the best services that act as market leaders. Experimental evaluation results demonstrate that the social network service selection and recommendation approach utilizing the credibility-based framework and “Follow the Leader” strategy provides an efficient, effective and scalable provision of credible services, especially for cold-start users. The research results take a further step towards developing a social-based automated and dynamically adaptive Web service selection and recommendation system in the future

Fact Checking in Community Forums

Community Question Answering (cQA) forums are very popular nowadays, as they represent effective means for communities around particular topics to share information. Unfortunately, this information is not always factual. Thus, here we explore a new dimension in the context of cQA, which has been ignored so far: checking the veracity of answers to particular questions in cQA forums. As this is a new problem, we create a specialized dataset for it. We further propose a novel multi-faceted model, which captures information from the answer content (what is said and how), from the author profile (who says it), from the rest of the community forum (where it is said), and from external authoritative sources of information (external support). Evaluation results show a MAP value of 86.54, which is 21 points absolute above the baseline.Comment: AAAI-2018; Fact-Checking; Veracity; Community-Question Answering; Neural Networks; Distributed Representation

Computational fact checking from knowledge networks

Traditional fact checking by expert journalists cannot keep up with the enormous volume of information that is now generated online. Computational fact checking may significantly enhance our ability to evaluate the veracity of dubious information. Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from Wikipedia. Statements independently known to be true consistently receive higher support via our method than do false ones. These findings represent a significant step toward scalable computational fact-checking methods that may one day mitigate the spread of harmful misinformation