Between Dec 19, 2024 and Jan 2, 2025, datasets can be submitted to DRUM but will not be processed until after the break. Staff will not be available to answer email during this period, and will not be able to provide DOIs until after Jan 2. If you are in need of a DOI during this period, consider Dryad or OpenICPSR. Submission responses to the UDC may also be delayed during this time.

Browsing by Subject "Computational Trust"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Computational trust in Multiplayer Online Games.
    (2012-04) Ahmad, Muhammad Aurangzeb
    Trust is a ubiquitous phenomenon in human societies. Computational trust refers to the mediation of trust via a computational infrastructure. It has been studied in a variety of contexts e.g., peer-to-peer systems, multi-agent systems, recommendation systems etc. While this is an active area of research, the types of questions that have been explored in this field has been limited mainly because of limitations in the types of datasets which are available to researchers. In this thesis questions related to trust in complex social environments represented by Massively Multiplayer Online Games (MMOGs) are explored. The main emphasis is that trust is a multi-level phenomenon both in terms of how it operates at multiple levels of network granularities and how trust relates to other social phenomenon like homophily, expertise, mentoring, clandestine behaviors etc. Social contexts and social environments affect not just the qualitative aspects of trust but this phenomenon is also manifested with respect to the network and structural signatures of trust network. Additionally trust is also explored in the context of predictive tasks: Previously described prediction tasks like link prediction are studied in the context of trust within the context of the link prediction family of problems: Link formation, link breakage, change in links etc. Additionally we define and explore new trust-related prediction problems i.e., trust propensity prediction, trust prediction across networks which can be generalized to the inter-network link prediction problem and success prediction based on using network measures of a person's social capital as a proxy.
  • Thumbnail Image
    Item
    The Role of Trust in Rumor Suppression on Social Media: A Multi-Method Approach Applying the Trust Scores in Social Media (TSM) Algorithm
    (2019-08) Kim, Hyejin
    Commercial rumor about an organization or brand, especially on social media, presents a special challenge for marketers and communication practitioners because of the fast flow and exchange of information among peers. Despite the importance of refuting rumors quickly and effectively, research on the effects and effectiveness of refuting rumor messages has been limited. To advance the literature in this emerging research area, the current project examines the impact of an interpersonal relational factor on the dissemination and effectiveness of rumor-refutation communication. The role of interpersonal relational factors is particularly important given that rumors spread through word-of-mouth (WOM) communication. Rumor-refutation communication should also utilize the same communication conduit for rapid and effective refutation. To advance rumor-refutation research and address the rising problem of commercial rumors, this dissertation project aimed to examine 1) how interpersonal influence among peers affects the belief and retransmission of rumors and rumor-refutation messages, and 2) potentially influential message characteristics that could help enhance interpersonal influence on readers’ belief of the rumors and rumor refutation messages, and the retransmission of rumor-refutation messages. To achieve these research goals, this study adopted a multi-method approach. Study 1 is a between-subjects repeated-measures experiment with a 2 (high-trustworthy vs. low-trustworthy source) x 2 (presence vs. absence of a trust cue) design that examines how the trustworthiness of the message source and a trust cue designed to induce message trustworthiness affect the belief and retransmission intention of rumors and rumor-refutation messages. Study 2 utilizes a computational research approach using the Trust Scores in Social Media (TSM) algorithm to test how mathematically captured trustworthiness scores of the sources of rumor-refutation messages influence actual message retransmission and how such an effect is moderated by the presence of trust cues included in the messages. The key findings in Study 1 revealed that a high-trustworthy source compared to a low-trustworthy source led to higher levels of rumor-refutation message belief and retransmission intention, but source trustworthiness did not affect the extent of reduction in the rumor belief and retransmission intention. Inclusion of a trust cue also did not moderate the impact of source trustworthiness on the belief and retransmission intention for both the rumor and rumor-refutation messages. Instead, it showed main effects in increasing the rumor-refutation message belief and retransmission intention, and a decreasing rumor belief and retransmission intention. The findings in Study 2 further confirmed the effects of the trustworthiness of the source on rumor-refutation message retransmission. However, inclusion of trust cues did not augment the source trustworthiness effects. The results indicated that the influence of source trustworthiness was stronger when the rumor-refutation message had no embedded trust cue. This study advances the rumor and electronic word-of-mouth research field by revealing how interpersonal influence among peer social media users can contribute to the effects and effectiveness of rumor-suppression communication. The findings also offer useful practical implications for identifying effective rumor-refutation dissemination hubs and refutation message-crafting strategies for a successful rumor-suppression campaign.