Causal studies on users’ behavioral choices in social networks
| dc.contributor.advisor | Bagheri, Ebrahim | |
| dc.contributor.advisor | Ghorbani, Ali A. | |
| dc.contributor.author | Falavarjani, Seyed Amin Mirlohi | |
| dc.date.accessioned | 2024-01-11T16:35:15Z | |
| dc.date.available | 2024-01-11T16:35:15Z | |
| dc.date.issued | 2022-08 | |
| dc.description.abstract | Causal inference is an essential topic across many domains, such as statistics, computer science, education, and economics, to name a few. The existence and convenience of obtaining appropriate observational data and the rapidly developing area of Big Data has enabled us to estimate causal effects between phenomena that was not previously possible. Scientists refer to causality as cause and effect where the cause, which can be an event, process, state, or object, is responsible for producing the effect, which is another event, process, state, or object. Causal inference is the process of determining a conclusion about a causal connection based on the conditions of the occurrence of an effect. This thesis proposes approaches to explore the potential causal effects of users’ different offline behaviors such as exercising, dining, shopping, and traveling on their alignment with social beliefs and emotions in online platforms. Additionally, this thesis examines whether being aligned with society is contagious. Concretely, this thesis considers the potential causal effects of users’ offline activities on their online social behavior. The objective of our work is to understand whether the activities that users are involved with in their real daily life, which place them within or away from social situations, have any direct causal impact on their behavior in online social networks. This work is motivated by the theory of normative social influence, which argues that individuals may show behaviors or express opinions that conform to those of the community for the sake of being accepted or from fear of rejection or isolation. Our main findings can be summarized as follows (1) a change in users’ offline behavior that affects the level of users’ exposure to social situations, e.g., starting to go to the gym or discontinuing frequenting bars, can have a causal impact on users’ online topical interests and sentiments; and (2) the causal relations between users’ socially situated offline activities and their online social behavior can be used to build effective predictive models of users’ online topical interests and sentiments. We further expand the state of the art by exploring the impact of social contagion on users’ social alignment, i.e., whether the decision to socially align oneself with the general opinion of the users on the social network is contagious to one’s connections on the network or not. This is an important problem as it explores whether users will make decisions to socially align themselves with others depending on whether their social network connections decide to socially align or not. The novelty of our work include: (1) unlike earlier work, our work is among the first to explore the contagiousness of the concept of social alignment on social networks; (2) our work adopts an instrumental variable approach to determine reliable causal relations between observed social contagion effects on the social network; (3) our work expands beyond the mere presence of contagion in social alignment and also explores the role of population heterogeneity on social alignment contagion. We find that a user’s decision to socially align or distance from social topics and sentiments influences the social alignment decisions of their connections on the social network. | |
| dc.description.copyright | © Seyed Amin Mirlohi Falavarjani, 2022 | |
| dc.format.extent | xiv, 155 | |
| dc.format.medium | electronic | |
| dc.identifier.uri | https://unbscholar.lib.unb.ca/handle/1882/37639 | |
| dc.language.iso | en | |
| dc.publisher | University of New Brunswick | |
| dc.rights | http://purl.org/coar/access_right/c_abf2 | |
| dc.subject.discipline | Computer Science | |
| dc.title | Causal studies on users’ behavioral choices in social networks | |
| dc.type | doctoral thesis | |
| oaire.license.condition | other | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | University of New Brunswick | |
| thesis.degree.level | doctorate | |
| thesis.degree.name | Ph.D. |