• Login
    View Item 
    •   DSpace@RPI Home
    • Rensselaer Libraries
    • RPI Theses Online (Complete)
    • View Item
    •   DSpace@RPI Home
    • Rensselaer Libraries
    • RPI Theses Online (Complete)
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Towards an understanding of information credibility on online social networks

    Author
    Sikdar, Sujoy Kumar
    View/Open
    175987_Sikdar_rpi_0185N_10616.pdf (1.319Mb)
    Other Contributors
    Adali, Sibel; Xia, Lirong; Magdon-Ismail, Malik;
    Date Issued
    2015-05
    Subject
    Computer science
    Degree
    MS;
    Terms of Use
    This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.;
    Metadata
    Show full item record
    URI
    https://hdl.handle.net/20.500.13015/1472
    Abstract
    A related task is that of identifying what pieces of information published on the social network are true. One approach to solve this problem treats humans on the social network as sensors with unknown reliability who sense the state of the world and report their observations as claims by publishing messages. Fact finding algorithms use an unsupervised estimation theoretic approach to jointly estimate the truthfulness of claims and the reliability of the human sensors that make the claims given some prior beliefs. However, due to the sparseness of information available in Twitter streaming data, these algorithms have very little information to update the prior beliefs for claims corroborated by very few sources. We find that using simple heuristics in developing fusion methods to use the credibility predictions yields improvements in performance over the estimates reached by the fact finder alone.; The increased adoption of online social networks such as Twitter has led to a deluge of available information. This brings about the need for methods to quickly identify and extract useful, credible information from large amounts of noisy data. We first show the challenges in defining credibility in the case of information in social media. Then, we develop supervised machine learning methods to extract credible information. We also define reasonable and meaningful credibility ground truth measures. To accomplish this, we deconstruct credibility and study the specific constructs that signal credibility individually. We then conduct a crowdsourced survey to collect ground truth credibility assessments. We find that surveys yield measurements that are often noisy and hard to work with. On Twitter, retweets are a form of endorsement by the users on Twitter and are a noisy in-network measure of credibility. We show that combining these measures yields ground truth measures where both sets of users agree on the credibility of a message. We find that models trained on these labeling schemes are able to identify more useful messages and achieve higher accuracy over models trained to predict the individual noisy ground truth values.;
    Description
    May 2015; School of Science
    Department
    Dept. of Computer Science;
    Publisher
    Rensselaer Polytechnic Institute, Troy, NY
    Relationships
    Rensselaer Theses and Dissertations Online Collection;
    Access
    Restricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.;
    Collections
    • RPI Theses Online (Complete)

    Browse

    All of DSpace@RPICommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    Login

    DSpace software copyright © 2002-2022  DuraSpace
    Contact Us | Send Feedback
    DSpace Express is a service operated by 
    Atmire NV