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A machine learning-based approach to predicting success of questions on social question-answering
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/asi.20215openalex-snapshot
A conceptual framework and open research questions for chat‐based reference service
inferredcites →https://doi.org/10.1002/asi.21676openalex-snapshot
The characteristics and motivations of health answerers for sharing information, knowledge, and experiences in online environments
inferredcites →https://doi.org/10.1002/meet.1450440256openalex-snapshot
Best‐answer selection criteria in a social Q&A site from the user‐oriented relevance perspective
inferredcites →https://doi.org/10.1002/meet.14504901302openalex-snapshot
Developing a typology of online Q&A models and recommending the right model for each question type
inferredcites →https://doi.org/10.1007/bf00153760openalex-snapshot
Information-Based Evaluation Criterion for Classifier's Performance
inferredcites →https://doi.org/10.1016/j.lisr.2009.07.006openalex-snapshot
Research agenda for social Q&A
inferredcites →https://doi.org/10.1016/s0306-4573(00)00055-8openalex-snapshot
Iterative exploration, design and evaluation of support for query reformulation in interactive information retrieval
inferredcites →https://doi.org/10.1080/13614560701711917openalex-snapshot
Seekers, sloths and social reference: Homework questions submitted to a question-answering community
inferredcites →https://doi.org/10.1145/1008992.1008996openalex-snapshot
Evaluating high accuracy retrieval techniques
inferredcites →https://doi.org/10.1145/1008992.1008998openalex-snapshot
Using temporal profiles of queries for precision prediction
inferredcites →https://doi.org/10.1145/1277741.1277864openalex-snapshot
Quantify query ambiguity using ODP metadata
inferredcites →https://doi.org/10.1145/1341531.1341557openalex-snapshot
Finding high-quality content in social media
inferredcites →https://doi.org/10.1145/1367497.1367561openalex-snapshot
Finding the right facts in the crowd
inferredcites →https://doi.org/10.1145/1367497.1367587openalex-snapshot
Knowledge sharing and yahoo answers
inferredcites →https://doi.org/10.1145/1753326.1753587openalex-snapshot
What do people ask their social networks, and why?
inferredcites →https://doi.org/10.1145/1835449.1835518openalex-snapshot
Evaluating and predicting answer quality in community QA
inferredcites →https://doi.org/10.1145/502115.502117openalex-snapshot
Scaling question answering to the web
inferredcites →https://doi.org/10.1353/pla.2005.0017openalex-snapshot
Evaluating the Quality of a Chat Service
inferredcites →https://doi.org/10.1609/icwsm.v5i1.14159openalex-snapshot
Factors Affecting Response Quantity, Quality, and Speed for Questions Asked Via Social Network Status Messages
inferredcites →https://doi.org/10.17723/aarc.64.1.q711461786663p33openalex-snapshot
A Virtual Expression of Need: An Analysis of E-mail Reference Questions
inferredcites →https://doi.org/10.21236/ada006655openalex-snapshot
Derivation of New Readability Formulas (Automated Readability Index, Fog Count and Flesch Reading Ease Formula) for Navy Enlisted Personnel
inferredcites →https://doi.org/10.5860/crl.76.3.251openalex-snapshot
Question-Negotiation and Information Seeking in Libraries
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/meet.14505001057openalex-snapshot
“I know what you are going through”: Answers to informational questions about eating disorders in Yahoo! answers: A qualitative study
inferred← citeshttps://doi.org/10.1002/meet.14505001101openalex-snapshot
“10 Points for the best answer!” – Baiting for explicating knowledge contributions within online Q&A
inferred← citeshttps://doi.org/10.1002/pra2.144openalex-snapshot
Questions or questioners: Factors affecting response quantity on academic social Q&A sites
inferred← citeshttps://doi.org/10.1002/pra2.2015.145052010042openalex-snapshot
The Quality and helpfulness of answers to eating disorder questions in yahoo! answers : Teens speak out
inferred← citeshttps://doi.org/10.1007/978-3-030-64452-9_37openalex-snapshot
Predicting Response Quantity from Linguistic Characteristics of Questions on Academic Social Q&A Sites
inferred← citeshttps://doi.org/10.1016/j.compedu.2024.105137openalex-snapshot
Digital social support among educators in social media: An international comparative study of tweets and replies in #teachertwitter and #twlz
inferred← citeshttps://doi.org/10.1016/j.ipm.2017.10.004openalex-snapshot
Questioner or question: Predicting the response rate in social question and answering on Sina Weibo
inferred← citeshttps://doi.org/10.1016/j.ipm.2018.09.005openalex-snapshot
The language of information need: Differentiating conscious and formalized information needs
inferred← citeshttps://doi.org/10.1108/lht-05-2021-0161openalex-snapshot
Characterizing response quantity on academic social Q&A sites: a multidiscipline comparison of linguistic characteristics of questions
inferred← citeshttps://doi.org/10.1109/hicss.2014.180openalex-snapshot
Questioning the Question -- Addressing the Answerability of Questions in Community Question-Answering
inferred← citeshttps://doi.org/10.1109/tale.2018.8615137openalex-snapshot
Integrated Telegram and Web-based Forum with Automatic Assessment of Questions and Answers for Collaborative Learning
inferred← citeshttps://doi.org/10.1145/3343413.3377948openalex-snapshot
Toward Quality Multimorbid Health Questions for Online Information Seeking in Q&A Sites
inferred← citeshttps://doi.org/10.1587/transinf.2022icp0006openalex-snapshot
Analysis of Non-Experts' Security- and Privacy-Related Questions on a Q&A Site
inferred← citeshttps://doi.org/10.3390/info13050214openalex-snapshot
Attention-Based Transformer-BiGRU for Question Classification
inferred← citeshttps://doi.org/10.4018/jgim.20211101.oa20openalex-snapshot
How Question Characteristics Impact Answer Outcomes on Social Question-and-Answer Websites
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