Retrieving people: Identifying potential answerers in Community Question‐Answering

Le, Long T.; Shah, Chirag

doi:10.1002/asi.24042

Cited by 14 publications

(5 citation statements)

References 50 publications

(51 reference statements)

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“…is work is like the Temporal Pro le Based Model (TPBM) as both rely on the Markov assumption for modelling the change of a given user from a current topic to a future topic. Le and Shah [31] have predicted the possible answerers based on the content of questions and pro les of users. In the proposed scheme, the authors have utilized the past activities for generating the user's pro le.…”

Section: Related Workmentioning

confidence: 99%

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Combining Latent Factor Model for Dynamic Recommendations in Community Question Answering Forums

Usman

Ahmad

Habib

et al. 2022

Computational Intelligence and Neuroscience

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Community Question Answering (CQA) web service provides a platform for people to share knowledge. Quora, Stack Overflow, and Yahoo! Answers are few sites where questioners post their queries and answerers respond to their respective queries. Due to the ease of use and quick responsiveness of the CQA platform, these sites are being widely adopted by the community. For better usability, there is a dire need to route the question toward the relevant answerers. To fulfil this gap, recommender systems play an important role in identifying the relevant answerers. To map the user interests more effectively, this research work proposed a dynamic feature representation of the latent user attributes for user profiling. The latent features are mapped by leveraging the Latent Dirichlet Allocation (LDA) for topic modelling of user data. The proposed recommendation model segments the user profile based on these latent user profiles incorporating the incremental learning of the users’ interests to produce the relevant recommendations in near real time. The experimental setup generated recommendation lists of variable sizes and evaluated using multiple evaluation metrics, such as mean average precision, recall, throughput, and different quality metrics, such as discounted cumulative gain and mean reciprocal rank. The results showed that the proposed model provided a better quality of recommendations in CQA forums, which is promising for future research in this domain.

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Section: Related Workmentioning

confidence: 99%

“…Evaluation metrics for our system are discussed in detail in this section compared with the baseline approach [31]. e authors have proposed an algorithm named as QRec for nding the potential answerers to the question.…”

Section: Evaluation Matricesmentioning

confidence: 99%

Combining Latent Factor Model for Dynamic Recommendations in Community Question Answering Forums

Usman

Ahmad

Habib

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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“…Information seeking community forums have been studied widely over the last two decades (e.g., [Liu et al, 2008;Li & King, 2010;Riahi et al, 2012;Zhang et al, 2014;Liu et al, 2015;Le & Shah, 2018]). However, human-to-human interaction in such platforms in terms of interaction with clarification questions is a new area of interest.…”

Section: Discussionmentioning

confidence: 99%

Analyzing clarification in asynchronous information‐seeking conversations

Tavakoli

Zamani

Scholer

et al. 2021

Asso for Info Science & Tech

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This research analyzes human-generated clarification questions to provide insights into how they are used to disambiguate and provide a better understanding of information needs. A set of clarification questions is extracted from posts on the Stack Exchange platform. Novel taxonomy is defined for the annotation of the questions and their responses. We investigate the clarification questions in terms of whether they add any information to the post (the initial question posted by the asker) and the accepted answer, which is the answer chosen by the asker. After identifying, which clarification questions are more useful, we investigated the characteristics of these questions in terms of their types and patterns. Non-useful clarification questions are identified, and their patterns are compared with useful clarifications. Our analysis indicates that the most useful clarification questions have similar patterns, regardless of topic. This research contributes to an understanding of clarification in conversations and can provide insight for clarification dialogues in conversational search scenarios and for the possible system generation of clarification requests in information-seeking conversations.

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“…Social network analyses could also be carried out to identify groupings among users. Additionally, algorithms, including those that seek to predict potential answerers (Le and Shah, 2018) could be used to identify high-risk individuals with a proclivity for spreading terrorist ideology.…”

Section: Discussionmentioning

confidence: 99%

The topic of terrorism on Yahoo! Answers: questions, answers and users’ anonymity

Chua

Banerjee

2019

AJIM

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Purpose The purpose of this paper is to explore the use of community question answering sites (CQAs) on the topic of terrorism. Three research questions are investigated: what are the dominant themes reflected in terrorism-related questions? How do answer characteristics vary with question themes? How does users’ anonymity relate to question themes and answer characteristics? Design/methodology/approach Data include 300 questions that attracted 2,194 answers on the community question answering Yahoo! Answers. Content analysis was employed. Findings The questions reflected the community’s information needs ranging from the life of extremists to counter-terrorism policies. Answers were laden with negative emotions reflecting hate speech and Islamophobia, making claims that were rarely verifiable. Users who posted sensitive content generally remained anonymous. Practical implications This paper raises awareness of how CQAs are used to exchange information about sensitive topics such as terrorism. It calls for governments and law enforcement agencies to collaborate with major social media companies to develop a process for cross-platform blacklisting of users and content, as well as identifying those who are vulnerable. Originality/value Theoretically, it contributes to the academic discourse on terrorism in CQAs by exploring the type of questions asked, and the sort of answers they attract. Methodologically, the paper serves to enrich the literature around terrorism and social media that has hitherto mostly drawn data from Facebook and Twitter.

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Retrieving people: Identifying potential answerers in Community Question‐Answering

Cited by 14 publications

References 50 publications

Combining Latent Factor Model for Dynamic Recommendations in Community Question Answering Forums

Combining Latent Factor Model for Dynamic Recommendations in Community Question Answering Forums

Analyzing clarification in asynchronous information‐seeking conversations

The topic of terrorism on Yahoo! Answers: questions, answers and users’ anonymity

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