Author: Brian S McGowan, PhD

Abstract

This article contributes to knowledge on how users establish the trustworthiness of digital information. An exploratory two-stage study was conducted with Master’s and undergraduate students in information studies. In the first phase of the study respondents commented on the factors and processes associated with trust formation. Participants commented on authorship and references, quality of writing and editing, and verification via links to external reference sources. Findings from the second phase, based on a checklist, suggested that participants relied on a range of factors when assessing the trustworthiness of articles, including content factors such as authorship, currency and usefulness together with context factors such as references, expert recommendation and triangulation with their own knowledge. These findings are discussed in the light of previous related research and recommendations for further research are offered.

via Understanding trust formation in digital information sources: The case of Wikipedia.

Abstract

In the recent explosion of Web information, it is important to find not only appropriate, but also more trustworthy answers to user questions. This paper proposes an improved ranking model for question answering (QA) which is focused on various answer trustworthiness factors. Contrary to past research that simply focused on document quality, we have identified three different answer trustworthiness factors in multiple layers of answering processes: document quality, authority and reputation of answer sources, and appropriateness of answering method for a given question. Each of these factors is used in the answer selection as an input to the ranking scheme that can be tuned for the confidence value for a particular answer candidate. Through several experiments, we analysed the efficacy of our QA model from two points of view: indexing and answering. In indexing, distilling unreliable documents brings not only a 96% reduction in document size but also a 92% speed increase in indexing time. To reveal the effect of trustworthiness factors in answering, we conducted several experiments to determine the optimum combination of weights of sub-features for trustworthiness factors. Finally, the proposed method using all answer trustworthiness factors obtained an improvement in effectiveness over the simple routing QA by 150% in Top1. We also investigated improvement impacts according to answer trustworthiness factors.

via Finding more trustworthy answers: Various trustworthiness factors in question answering.

Abstract: This study examines the strategies employed in justifying counter-arguments against answers provided in Q&A (question and answer) discussion. The study also explores how information sources are used in support of such arguments. The findings draw on the analysis of 100 discourse episodes focusing on global warming – a controversial topic eliciting competing answers from the participants. The empirical data were downloaded from Yahoo! Answers. Four strategies were identified. Of them, questioning the validity of answers and questioning the background assumptions of answers were used most frequently. While justifying counter-arguments, the participants also drew on emotional appeals and questioned the contributor’s motives. To support the counter-arguments, the participants mainly drew on internet-based sources of information. The controversial nature of the discussion topic was reflected in the debate about the most authoritative information sources: persuasive material advocating a particular viewpoint to global warming vs objective research reports.

via Strategies for justifying counter-arguments in Q&A discussion.

Abstract

Although diverse groups argue about the potential and true value benefits from social-media big data, there is no doubt that the era of big data exploitation has begun, driving the development of novel data-centric applications. Big data is notable not only because of its size, but also because of the complexity caused by its relationality to other data. In the past, owing to the limited possibilities of accessing big data, few data sources were available to allow researchers to develop advanced data-driven applications, such as monitoring of emerging real-world events. In fact, social media is greatly impacting the growth of big data; and big data is providing enterprises with the data to help them understand how to better detect marketing demands. Microblogging is a social network service capable of aggregating messages to explore facts and unknown knowledge. Nowadays, people often attempt to search for trending news and hot topics in real time from microblogging messages to satisfy their information needs. Under such a circumstance, a real demand is to find a way to allow users to organize a large number of microblogging messages into understandable events. In this work, we attempt to tackle such challenges by developing an online text-stream clustering approach using a modified density-based clustering model with collected microblogging big data. The system kernel combines three technical components, including a dynamic term weighting scheme, a neighbourhood generation algorithm and an online density-based clustering technique. After acquiring detected event topics by the system, our system provides functions for recommending top-priority event information to assist people to effectively organize emerging event data through the developed topic ranking algorithm.

via Leveraging microblogging big data with a modified density-based clustering approach for event awareness and topic ranking.