With the growing use of Online social media (OSM), users are observing a substantial amount of information in their social feeds. Moreover, people often use multiple OSM platforms because of each platform's unique features. Because of the huge volume of information present in social feeds, it restricts a user's ability to process the relevant information since the most important information may be overwhelmed by unimportant information. For information to diffuse in OSM, the receiver's attention is the most important condition. Cognition plays an important role in determining a user's attention/responsiveness. If OSM users receive the information above their cognition limits and become less responsive, they are in an information-overloaded state. I believe there are several factors that can lead OSM users to be in an information overload state. The main purpose of this study is to explore some of the factors that can affect the ability of OSM users to respond to information while they are overloaded. In this study, I explore two major factors: (1) Influence Gradient i.e., the differences in the magnitude of influence exerted and influence experienced by each OSM user who is active in either single or multi-platform OSM, and (2) Emotion Contagion i.e., the effects on users' response capacity due to the emotional stimuli in the content of social media messages delivered from the influencers to their receivers. Experiments are designed using Information Overload Model (IOM), which quantifies an individual's current information processing capacity (IPC), and the Multi-Action Cascade Model (MACM) which simulates the information diffusion on social media. IOM is implemented into the agents of the MACM model to simulate information diffusion phenomena. They are incorporated with memory, and their IPC can be quantified along with the flow of information into the network such as the amount of information they stored to respond, new messages they received from their influencers, and the information overload they experience. Transfer entropy is used to quantify peer influence between the users in single or multi-platform OSM. I use empirical data from GitHub, Twitter, and YouTube. For GitHub, I use repositories related to the cryptocurrency community. Twitter and YouTube data is extracted from profiles engaged in narratives related to China-Pakistan economic corridor under one belt one road initiative. For the emotion contagion experiment, I use Twitter data on Venezuela's election dispute in early 2019. From this research work, I find evidence that negative influence gradients lower the IPC of OSM users active in both single and multi-platform. This shows that users who are influenced more than they can exert their influence on others are the ones to be overloaded. While the users exerting more influence on others can function at full capacity. I found that there exists emotional contagion from the influencers to receivers and un-overloaded users are affected by such contagion. Overloaded users are not supposed to be affected by emotional contagion. Interestingly, I found that overloaded users are still affected by negative emotions and chose to respond to them and ignore messages with positive emotions.
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Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Industrial Engineering and Management Systems
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Baral, Nisha, "Investigating the Effects of Negative Influence Gradients and Emotion Contagion on the Information Processing Capacity of Social Media Users: Information Diffusion Modeling Approach" (2022). Electronic Theses and Dissertations, 2020-. 1460.