Review Paper on Answers Selection and Recommendation in Community Question Answers System

Nowadays, question answering system is more convenient for the users, users ask question online and then they will get the answer of that question, but as browsing is primary need for each an individual, the number of users ask question and system will provide answer but the computation time increased as well as waiting time increased and same type of questions are asked by different users, system need to give same answers repeatedly to different users. To avoid this we propose PLANE technique which may quantitatively rank answer candidates from the relevant question pool. If users ask any question, then system provide answers in ranking form, then system recommend highest rank answer to the user. We proposing expert recommendation system, an expert will provide answer of the question which is asked by the user and we also implement sentence level clustering technique in which a single question have multiple answers, system provide most suitable answer to the question which is asked by the user

Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A Platforms

Question categorization and expert retrieval methods have been crucial for information organization and accessibility in community question & answering (CQA) platforms. Research in this area, however, has dealt with only the text modality. With the increasing multimodal nature of web content, we focus on extending these methods for CQA questions accompanied by images. Specifically, we leverage the success of representation learning for text and images in the visual question answering (VQA) domain, and adapt the underlying concept and architecture for automated category classification and expert retrieval on image-based questions posted on Yahoo! Chiebukuro, the Japanese counterpart of Yahoo! Answers. To the best of our knowledge, this is the first work to tackle the multimodality challenge in CQA, and to adapt VQA models for tasks on a more ecologically valid source of visual questions. Our analysis of the differences between visual QA and community QA data drives our proposal of novel augmentations of an attention method tailored for CQA, and use of auxiliary tasks for learning better grounding features. Our final model markedly outperforms the text-only and VQA model baselines for both tasks of classification and expert retrieval on real-world multimodal CQA data.Comment: Submitted for review at CIKM 201

Disagreeable Privacy Policies: Mismatches between Meaning and Users’ Understanding

Privacy policies are verbose, difficult to understand, take too long to read, and may be the least-read items on most websites even as users express growing concerns about information collection practices. For all their faults, though, privacy policies remain the single most important source of information for users to attempt to learn how companies collect, use, and share data. Likewise, these policies form the basis for the self-regulatory notice and choice framework that is designed and promoted as a replacement for regulation. The underlying value and legitimacy of notice and choice depends, however, on the ability of users to understand privacy policies. This paper investigates the differences in interpretation among expert, knowledgeable, and typical users and explores whether those groups can understand the practices described in privacy policies at a level sufficient to support rational decision-making. The paper seeks to fill an important gap in the understanding of privacy policies through primary research on user interpretation and to inform the development of technologies combining natural language processing, machine learning and crowdsourcing for policy interpretation and summarization. For this research, we recruited a group of law and public policy graduate students at Fordham University, Carnegie Mellon University, and the University of Pittsburgh (“knowledgeable users”) and presented these law and policy researchers with a set of privacy policies from companies in the e-commerce and news & entertainment industries. We asked them nine basic questions about the policies’ statements regarding data collection, data use, and retention. We then presented the same set of policies to a group of privacy experts and to a group of non-expert users. The findings show areas of common understanding across all groups for certain data collection and deletion practices, but also demonstrate very important discrepancies in the interpretation of privacy policy language, particularly with respect to data sharing. The discordant interpretations arose both within groups and between the experts and the two other groups. The presence of these significant discrepancies has critical implications. First, the common understandings of some attributes of described data practices mean that semi-automated extraction of meaning from website privacy policies may be able to assist typical users and improve the effectiveness of notice by conveying the true meaning to users. However, the disagreements among experts and disagreement between experts and the other groups reflect that ambiguous wording in typical privacy policies undermines the ability of privacy policies to effectively convey notice of data practices to the general public. The results of this research will, consequently, have significant policy implications for the construction of the notice and choice framework and for the US reliance on this approach. The gap in interpretation indicates that privacy policies may be misleading the general public and that those policies could be considered legally unfair and deceptive. And, where websites are not effectively conveying privacy policies to consumers in a way that a “reasonable person” could, in fact, understand the policies, “notice and choice” fails as a framework. Such a failure has broad international implications since websites extend their reach beyond the United States

FACTORS INFLUENCING USER’S CONTINUANCE INTENTION ON PAID QUESTION AND ANSWER SERVICE ----A STUDY ON WEIBO IN CHINA

This thesis addresses the research question “Why do users continue to use paid Q&A in China” by means showed below: First, this research introduces research background of paid Q&A in China and raises corresponding research question and highlights the research significance of this thesis topic; Second, the author concludes previous research on paid Q&A in aspects of Q&A system, paid subscription and sharing economy, and finds that most of prior research focuses on exploring the influence of usefulness but not enjoyment on the users’ willingness of continuing using a paid Q&A system; Third, the thesis introduces the VAM theory and build a modified model based on it, this modified model highlights the importance of pleasure on users’ continuance intention in using paid Q&A; Finally, the empirical study combining an Exploratory Factor Analysis and a Confirmatory Factor Analysis proves that, after integrating factors extracted from previous research and the proposed model, the research is tested to be explanatorily capable and hypotheses related to the model are mostly proved to be supported. As a conclusion, this study conducts an investigation on the constructs and related theories that influence users’ continuance intention to use paid Q&A, from a hedonic perspective. In this thesis, VAM theory is selected as the prototype of proposed research model which reveals factors affecting users’ continuance intention to use a Chinese paid Q&A product named Weibo Paid Q&A. In this thesis, the proposed model makes predictions that the constructs perceived fee and community atmosphere along with perceived enjoyment construct have critical effect on users’ continuance willingness in using Weibo Paid Q&A in China. With the assistance of PLS–SEM, this study analyzes data collected from users in WPQA, the empirical study verifies that users' continuance intention is assuredly dependent on perceived fee and community atmosphere along with perceived enjoyment. The study also reveals that quality of answerers and quality of answer positively exert significant influences on perceived enjoyment

The big five: Discovering linguistic characteristics that typify distinct personality traits across Yahoo! answers members

Indexación: Scopus.This work was partially supported by the project FONDECYT “Bridging the Gap between Askers and Answers in Community Question Answering Services” (11130094) funded by the Chilean Government.In psychology, it is widely believed that there are five big factors that determine the different personality traits: Extraversion, Agreeableness, Conscientiousness and Neuroticism as well as Openness. In the last years, researchers have started to examine how these factors are manifested across several social networks like Facebook and Twitter. However, to the best of our knowledge, other kinds of social networks such as social/informational question-answering communities (e.g., Yahoo! Answers) have been left unexplored. Therefore, this work explores several predictive models to automatically recognize these factors across Yahoo! Answers members. As a means of devising powerful generalizations, these models were combined with assorted linguistic features. Since we do not have access to ask community members to volunteer for taking the personality test, we built a study corpus by conducting a discourse analysis based on deconstructing the test into 112 adjectives. Our results reveal that it is plausible to lessen the dependency upon answered tests and that effective models across distinct factors are sharply different. Also, sentiment analysis and dependency parsing proven to be fundamental to deal with extraversion, agreeableness and conscientiousness. Furthermore, medium and low levels of neuroticism were found to be related to initial stages of depression and anxiety disorders. © 2018 Lithuanian Institute of Philosophy and Sociology. All rights reserved.https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/275