20,971 research outputs found
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
Detecting Experts on Quora: By their Activity, Quality of Answers, Linguistic Characteristics and Temporal Behaviors
Question and answering sites are useful in sharing the knowledge by answering questions. It is a medium of sharing knowledge. Quora is the fastest emerging effective Q&A site, which is the best source of knowledge. Here you can ask a question, and get help in getting answers from people with firsthand experience, and blog about what you know. In this paper, we are investigating and identifying potential experts who are providing the best solutions to the questioner needs. We have considered several techniques in identifying user as an expert or non-expert. We have targeted the most followed topics in Quora and finally came up with five topics: Mathematics, Politics, Technology, Sports and Business. We then crawled the user profiles who are following these topics. Each topic dataset has many special features. Our research indicates that experts are quite different from normal users and tend to produce high quality answers to as many questions as possible to gain their reputation. After evaluation, we got a limited number of experts who have potential expertise in specific fields, achieving up to 97% accuracy and 0.987 AUC
Simplifying Sparse Expert Recommendation by Revisiting Graph Diffusion
Community Question Answering (CQA) websites have become valuable knowledge
repositories where individuals exchange information by asking and answering
questions. With an ever-increasing number of questions and high migration of
users in and out of communities, a key challenge is to design effective
strategies for recommending experts for new questions. In this paper, we
propose a simple graph-diffusion expert recommendation model for CQA, that can
outperform state-of-the art deep learning representatives and collaborative
models. Our proposed method learns users' expertise in the context of both
semantic and temporal information to capture their changing interest and
activity levels with time. Experiments on five real-world datasets from the
Stack Exchange network demonstrate that our approach outperforms competitive
baseline methods. Further, experiments on cold-start users (users with a
limited historical record) show our model achieves an average of ~ 30%
performance gain compared to the best baseline method
Marine Oil Snow Sedimentation and Flocculent Accumulation (MOSSFA) Workshop
The MOSSFA hypothesis is that the formation of marine snow/oil aggregates and its accumulation at the seafloor is related to events associated with the oil spill, various mitigation measures (e.g., the use of dispersants and in situ burning), and increased sediment-laden fresh water releases from Mississippi River impoundments. If this hypothesis is correct then this phenomenon takes on an added global significance as 85% of deep-water oil exploration occurs adjacent to deltaic systems. To better understand the sequence of events and the oceanographic processes involved, three of the Gulf of Mexico Research Initiative (GoMRI)
funded Centers (C-IMAGE, DEEP-C and ECOGIG), all of which have Principal Investigators involved in the various aspects of the MOSSFA question, received funding to conduct two workshops related to Marine
Oil Snow Sedimentation Flocculent Accumulation (MOSSFA). The intent of the first workshop was to
bring together researchers working on MOSSFA to provide a synthesis of known facts, identify data gaps and propose follow-up research to help resolve key questions and uncertainties regarding the MOSSFA hypothesis
Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform
Many platforms collect crowdsourced information primarily from volunteers. As
this type of knowledge curation has become widespread, contribution formats
vary substantially and are driven by diverse processes across differing
platforms. Thus, models for one platform are not necessarily applicable to
others. Here, we study the temporal dynamics of Genius, a platform primarily
designed for user-contributed annotations of song lyrics. A unique aspect of
Genius is that the annotations are extremely local -- an annotated lyric may
just be a few lines of a song -- but also highly related, e.g., by song, album,
artist, or genre. We analyze several dynamical processes associated with lyric
annotations and their edits, which differ substantially from models for other
platforms. For example, expertise on song annotations follows a ``U shape''
where experts are both early and late contributors with non-experts
contributing intermediately; we develop a user utility model that captures such
behavior. We also find several contribution traits appearing early in a user's
lifespan of contributions that distinguish (eventual) experts from non-experts.
Combining our findings, we develop a model for early prediction of user
expertise.Comment: 9 pages. 10 figure
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Challenges to the Integration of Renewable Resources at High System Penetration
Successfully integrating renewable resources into the electric grid at penetration levels to meet a 33 percent Renewables Portfolio Standard for California presents diverse technical and organizational challenges. This report characterizes these challenges by coordinating problems in time and space, balancing electric power on a range of scales from microseconds to decades and from individual homes to hundreds of miles. Crucial research needs were identified related to grid operation, standards and procedures, system design and analysis, and incentives, and public engagement in each scale of analysis. Performing this coordination on more refined scales of time and space independent of any particular technology, is defined as a “smart grid.” “Smart” coordination of the grid should mitigate technical difficulties associated with intermittent and distributed generation, support grid stability and reliability, and maximize benefits to California ratepayers by using the most economic technologies, design and operating approaches
Facilitating Efficient Information Seeking in Social Media
abstract: Online social media is popular due to its real-time nature, extensive connectivity and a large user base. This motivates users to employ social media for seeking information by reaching out to their large number of social connections. Information seeking can manifest in the form of requests for personal and time-critical information or gathering perspectives on important issues. Social media platforms are not designed for resource seeking and experience large volumes of messages, leading to requests not being fulfilled satisfactorily. Designing frameworks to facilitate efficient information seeking in social media will help users to obtain appropriate assistance for their needs
and help platforms to increase user satisfaction.
Several challenges exist in the way of facilitating information seeking in social media. First, the characteristics affecting the user’s response time for a question are not known, making it hard to identify prompt responders. Second, the social context in which the user has asked the question has to be determined to find personalized responders. Third, users employ rhetorical requests, which are statements having the
syntax of questions, and systems assisting information seeking might be hindered from focusing on genuine questions. Fouth, social media advocates of political campaigns employ nuanced strategies to prevent users from obtaining balanced perspectives on
issues of public importance.
Sociological and linguistic studies on user behavior while making or responding to information seeking requests provides concepts drawing from which we can address these challenges. We propose methods to estimate the response time of the user for a given question to identify prompt responders. We compute the question specific social context an asker shares with his social connections to identify personalized responders. We draw from theories of political mobilization to model the behaviors arising from the strategies of people trying to skew perspectives. We identify rhetorical questions by modeling user motivations to post them.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests
Various forms of Peer-Learning Environments are increasingly being used in
post-secondary education, often to help build repositories of student generated
learning objects. However, large classes can result in an extensive repository,
which can make it more challenging for students to search for suitable objects
that both reflect their interests and address their knowledge gaps. Recommender
Systems for Technology Enhanced Learning (RecSysTEL) offer a potential solution
to this problem by providing sophisticated filtering techniques to help
students to find the resources that they need in a timely manner. Here, a new
RecSysTEL for Recommendation in Peer-Learning Environments (RiPLE) is
presented. The approach uses a collaborative filtering algorithm based upon
matrix factorization to create personalized recommendations for individual
students that address their interests and their current knowledge gaps. The
approach is validated using both synthetic and real data sets. The results are
promising, indicating RiPLE is able to provide sensible personalized
recommendations for both regular and cold-start users under reasonable
assumptions about parameters and user behavior.Comment: 25 pages, 7 figures. The paper is accepted for publication in the
Journal of Educational Data Minin
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