Tie strength in question &amp; answer on social network sites

Panovich, Katrina; Miller, Rob; Karger, David R.

doi:10.1145/2145204.2145361

Cited by 91 publications

(61 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Panovich et al [34] studied the role of tie strength in answering questions. Through user studies they found that answers from users with whom the questioner shares stronger ties provide slightly more information than those from people with weaker ties.…”

Section: Related Workmentioning

confidence: 99%

Automatic evaluation of information provider reliability and expertise

et al. 2013

View full text Add to dashboard Cite

Q&A social media have gained a lot of attention during the recent years. People rely on these sites to obtain information due to a number of advantages they offer as compared to conventional sources of knowledge (e.g., asynchronous and convenient access). However, for the same question one may find highly contradicting answers, causing an ambiguity with respect to the correct information. This can be attributed to the presence of unreliable and/or non-expert users. These two attributes (reliability and expertise) significantly affect the quality of the answer/information provided. We present a novel approach for estimating these user's characteristics relying on human cognitive traits. In brief, we propose each user to monitor the activity of his peers (on the basis of responses to questions asked by him) and observe their compliance with predefined cognitive models. These observations lead to local assessments that can be further fused to obtain a reliability and expertise consensus for every other user in the social network (SN). For the aggregation part we use subjective logic. To the best of our knowledge this is the first study of this kind in the context of Q&A SNs. Our proposed approach is highly distributed; each user can individually estimate the expertise and the reliability of his peers using his direct interactions with them and our framework. The online SN (OSN), which can be considered as a distributed database, performs continuous data aggregation for users expertise and reliability assesment in order to reach a consensus. In our evaluations, we first emulate a Q&A SN to examine various performance aspects of our algorithm (e.g., convergence time, responsiveness etc.). Our evaluations indicate that it can accurately assess the reliability and the expertise of a user with a small number of samples and can successfully react to the latter's behavior change, provided that the cognitive traits hold in practice. Furthermore, the use of the consensus operator for the aggregation of multiple opinions on a specific user, reduces the uncertainty with regards to the final assessment. However, as real data obtained from Yahoo! Answers imply, the pairwise interactions between specific users are limited. Hence, we consider the aggregate set of questions as posted from the system itself and we assess the expertise and realibility of users based on their response behavior. We observe, that users have different behaviors depending on the level at which we are observing them. In particular, while their activity is focused on a few general categories, yielding them reliable, their microscopic (within general category) activity is highly scattered.

show abstract

Section: Related Workmentioning

confidence: 99%

Automatic evaluation of information provider reliability and expertise

et al. 2013

View full text Add to dashboard Cite

show abstract

“…This line of investigation has also explored social network analysis to benefit CQA service. For example, Katrina et al [12] presented a study relating tie strength to answer quality for questions asked on social network sites and they found that stronger ties actually provided better answers for some measures of answer quality.…”

Section: Related Workmentioning

confidence: 99%

Towards human-centric personalized expertise ranking in community-based question answering

Yang

Lua

Wang

2013

Proceedings of the 2013 ACM SIGCOMM Workshop on Future Human-Centric Multimedia Networking

View full text Add to dashboard Cite

Search engine has been the major source for discovering user-generated content with authority, not only on contentcentric multimedia but also on human-centric social networks. Many studies have demonstrated the power of graphbased ranking algorithms to propagate reputation and expertise along social graph composed of users' links for information search, to promote experts and demote spams. However, these existing works shed little light on personalized expertise ranking algorithm from view of the topic-relevance between users' interests. In this study, we demonstrated the existence of homophily in users' interests measured by social annotations using AskMeFi, a large scale communitydriven question and answering (CQA) system. We discovered that best answers as rated by questioners (users posting questions), are inclined to arrive promptly from co-interest users with authority and topic-relevance after the questions are posted. We proposed Human-centric Personalized Expertise Ranking, a graph-based algorithm which takes the topic-relevance and authority among co-interest users and time traits into the computation of the expertise level of users. The experimental results revealed that our proposed algorithm significantly outperforms other non-personalized expertise ranking algorithms.

show abstract

“…Those seeking information and support online, particularly through SNSs, are likely to be able to access a much higher number of sources and answers to questions than they would through face-to-face contact [18] due to having access to a larger pool of contacts [19]. Furthermore, the sources of information people access online may be considered more reliable, accurate or trustworthy by information seekers [20].…”

Section: Introductionmentioning

confidence: 99%

Qualitative analysis of UK and USA open mental health Facebook groups for mental health support (Preprint)

Prescott¹,

Mack²,

Brown³

2018

Preprint

View full text Add to dashboard Cite

Background: Facebook is still the most popular social networking site in the world. A growing number of people with mental health issues are using open Facebook groups as a source of informational and emotional support, and to provide peer support to others with mental illness. Facebook groups specifically tailored for mental health can provide a safe, less stigmatised environment for people to share their personal experiences with others who understand, and gain information and advice from those with similar experiences. The peer support people gain from other group members can reduce feelings of isolation, helping them feel more connected and less alone.

show abstract

Tie strength in question & answer on social network sites

Cited by 91 publications

References 22 publications

Automatic evaluation of information provider reliability and expertise

Automatic evaluation of information provider reliability and expertise

Towards human-centric personalized expertise ranking in community-based question answering

Qualitative analysis of UK and USA open mental health Facebook groups for mental health support (Preprint)

Contact Info

Product

Resources

About