The fast expanding of social media fuels the spreading of misinformation which disrupts people's normal lives. It is urgent to achieve goals of misinformation identification and early detection in social media. In dynamic and complicated social media scenarios, some conventional methods mainly concentrate on feature engineering which fail to cover potential features in new scenarios and have difficulty in shaping elaborate high-level interactions among significant features. Moreover, a recent Recurrent Neural Network (RNN) based method suffers from deficiencies that it is not qualified for practical early detection of misinformation and poses a bias to the latest input. In this paper, we propose a novel method, Convolutional Approach for Misinformation Identification (CAMI) based on Convolutional Neural Network (CNN). CAMI can flexibly extract key features scattered among an input sequence and shape high-level interactions among significant features, which help effectively identify misinformation and achieve practical early detection. Experiment results on two large-scale datasets validate the effectiveness of CAMI model on both misinformation identification and early detection tasks.

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A convolutional approach for misinformation identification

To explore the effects of different incentives on crowdsourcing participation and submission quality, we conduct a randomized field experiment on Taskcn, a large Chinese crowdsourcing site using mechanisms with features of an all-pay auction. In our study, we systematically vary the size of the reward as well as the presence of a soft reserve, or early high-quality submission. We find that a higher reward induces significantly more submissions and submissions of higher quality. In comparison, we find that high-quality users are significantly less likely to enter tasks where a high-quality solution has already been submitted, resulting in lower overall quality in subsequent submissions in such soft reserve treatments.

Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1845 .

This paper was accepted by Uri Gneezy, behavioral economics.

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Crowdsourcing with All-Pay Auctions: A Field Experiment on Taskcn

The increasingly widespread use of social network sites to expand and deepen one's social connections is a relatively new but potentially important phenomenon that has implications for teaching and learning and teacher education in the 21st century. This paper surveys the educational research literature to examine: How such technologies are perceived and used by K-12 learners and teachers with what impacts on pedagogy or students' learning. Selected studies were summarized and categorized according to the four types introduced by Roblyer (2005) as studies most needed to move the educational technology field forward. These include studies that establish the technology's effectiveness at improving student learning; investigate implementation strategies; monitor social impact; and report on common uses to shape the direction of the field. We found the most prevalent type of study conducted related to our focal topic was research on common uses. The least common type of study conducted was research that established the technology's effectiveness at improving student learning. Implications for the design of future research and teacher education initiatives are discussed.

Learning and teaching with social network sites: A decade of research in K-12 related education

Knowledge Management (KM) is a diffuse and controversial term, which has been used by a large number of research disciplines. CSCW, over the last 20 years, has taken a critical stance towards most of these approaches, and instead, CSCW shifted the focus towards a practice-based perspective. This paper surveys CSCW researchers' viewpoints on what has become called `knowledge sharing' and `expertise sharing'. These are based in an understanding of the social contexts of knowledge work and practices, as well as in an emphasis on communication among knowledgeable humans. The paper provides a summary and overview of the two strands of knowledge and expertise sharing in CSCW, which, from an analytical standpoint, roughly represent `generations' of research: an `object-centric' and a `people-centric' view. We also survey the challenges and opportunities ahead.

Sharing Knowledge and Expertise: The CSCW View of Knowledge Management

Community question-answering (CQA) systems, such as Yahooe Answers or Stack Overflow, belong to a prominent group of successful and popular Web 2.0 applications, which are used every day by millions of users to find an answer on complex, subjective, or context-dependent questions. In order to obtain answers effectively, CQA systems should optimally harness collective intelligence of the whole online community, which will be impossible without appropriate collaboration support provided by information technologies. Therefore, CQA became an interesting and promising subject of research in computer science and now we can gather the results of 10 years of research. Nevertheless, in spite of the increasing number of publications emerging each year, so far the research on CQA systems has missed a comprehensive state-of-the-art survey. We attempt to fill this gap by a review of 265 articles published between 2005 and 2014, which were selected from major conferences and journals. According to this evaluation, at first we propose a framework that defines descriptive attributes of CQA approaches. Second, we introduce a classification of all approaches with respect to problems they are aimed to solve. The classification is consequently employed in a review of a significant number of representative approaches, which are described by means of attributes from the descriptive framework. As a part of the survey, we also depict the current trends as well as highlight the areas that require further attention from the research community.

A Comprehensive Survey and Classification of Approaches for Community Question Answering

Online question-answering services provide mechanisms for knowledge exchange by allowing users to ask and answer questions on a wide range of topics. A key question for designing such services is whether charging a price has an effect on answer quality. Two field experiments using one such service, Google Answers, offer conflicting answers to this question. To resolve this inconsistency, we re-analyze data from Harper et al. [5] and Chen et al. [2] to study the price effect in greater depth. Decomposing the price effect into two different levels yields results that reconcile those of the two field experiments. Specifically, we find that: (1) a higher price significantly increases the likelihood that a question receives an answer and (2) for questions that receive an answer, there is no significant price effect on answer quality. Additionally, we find that the rater background makes a difference in evaluating answer quality.

http://dmrussell.net/CHI2010/docs/p325.pdf

Re-examining price as a predictor of answer quality in an online q&a site

With the evolution of the Web and development of web-based search engines, online searching has become a common method for obtaining information. Given this popularity, the question arises as to how much time people save by using search engines for their information needs compared to offline sources, as well as how online searching affects both search experiences and search outcomes. Using a random sample of queries from a major search engine and a sample of reference questions from the Internet Public Library (IPL), we conduct a real-effort experiment to compare online and offline search experiences and outcomes. We find that participants are significantly more likely to find an answer on the Web (100 %), compared to offline searching (between 87 % and 90 %). Restricting our analysis to the set of questions in which participants find answers in both treatments, a Web search takes on average 7 (9) minutes, whereas the corresponding offline search takes 22 (19) minutes for a search-engine (IPL) question. Furthermore, while raters judge library sources to be significantly more trustworthy and authoritative than the corresponding Web sources, they judge Web sources as significantly more relevant. Balancing all factors, we find that the overall source quality is not significantly different between the two treatments for the set of search-engine questions. However, for IPL questions, we find that non-Web sources are judged to have significantly higher overall quality than the corresponding Web sources. In comparison, for factual questions, Web search results are significantly more likely to be correct (66 % vs. 43 %). Lastly, post-search questionnaires reveal that participants find online searching more enjoyable than offline searching.

https://yanchen.people.si.umich.edu/papers/VOS_2013_03.pdf

A day without a search engine: an experimental study of online and offline searches

This study examines how bloggers establish and enhance the credibility of their blogs through a series of blogging practices. Based on an analysis of interviews with 22 independent bloggers who blog on a range of topics, we present audience-aware credibility as a theoretical construct. Audience-aware credibility is defined as how bloggers signal their credibility based on who they think their audience is and how they provide value to that perceived audience. The analysis of bloggers’ credibility constructs, conceptualizations of audience, and perceived blog value identified four types of bloggers who constructed audience-aware credibility in distinctive ways: Community Builder, Expertise Provider, Topic Synthesizer, and Information Filterer. We then report on these bloggers’ blogging practices for establishing credibility and strategies for interacting with their audience to enhance credibility. The contributions of this study are to expand credibility constructs for social media research and to demonstrate the role of credibility perceptions in content contributors’ online activities. The findings reveal that a multi-dimensional construct of audience-aware credibility serves as a driving factor influencing and shaping blogging practices of all four types of bloggers. Copyright © 2014, Association for the Advancement of Artificial Intell igence (www.aaai.org). All rights reserved.

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Audience-aware credibility: From understanding audience to establishing credible blogs

In this paper, we examine the value of social question-answering (Q&A) services as a platform for social search. We present a quasi-field study where we instructed 20 study participants to use a social Q&A service, Yahoo! Answers, for a period of one week, and interviewed them about their experience with Yahoo! Answers based on the questions (N=99) they posted to the site. The results indicate that participants turned to a social search system when they needed firsthand information, diverse perspectives, and others' value judgments. Participants also preferred social search systems over web search engines in situations where they could obtain tailored information, access original and non-popular information, filter out information, and interact with real people. Various strategies that participants employed to ensure that their questions would be likely to be answered were also identified. This study contributes to the field of information science by investigating a social Q&A service using the framework of social search from the information seeker's perspective. The results have implications for developers and designers of social search systems.

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The value of social search: seeking collective personal experience in social Q&A

Individuals may encounter distinct kinds of challenges in assessing credibility in a social Q&A setting where they interact with strangers. It is necessary to better understand how people make credibility judgments when seeking information using social Q&A services because people increasingly use such services to obtain personalized answers from a large pool of unknown people. In this paper, we report preliminary findings from a quasi-field study where participants were asked to use Yahoo! Answers for one week and were interviewed afterwards. We find that participants’ assessment of the credibility of strangers who answered their questions occurred in three different dimensions: attitude, trustworthiness, and expertise. Furthermore, different elements were noticed and interpreted in each dimension of the credibility assessment. Our work provides insights into source credibility assessment in social Q&A settings and implications for the design of social technologies that better support people’s online credibility assessment.

/pdf/answers-from-the-crowd-how-credible-are-strangers-in-social-2umniea0ve.pdf

Grace YoungJoo Jeon

Papers

Re-examining price as a predictor of answer quality in an online q&a site

A day without a search engine: an experimental study of online and offline searches

Audience-aware credibility: From understanding audience to establishing credible blogs

The value of social search: seeking collective personal experience in social Q&A

Answers from the crowd: How credible are strangers in social Q&A?