<div><div><div><p>Many complex processes can be viewed as sequential events on a network. In this thesis, we study the interplay between a network and the event sequences on it. We first focus on predicting events on a known network. Examples of such include: modeling retweet cascades, forecasting earthquakes, and tracing the source of a pandemic. In specific, given the network structure, we solve two types of problems - (1) forecasting future events based on the historical events, and (2) identifying the initial event(s) based on some later observations of the dynamics. The inverse problem of inferring the unknown network topology or links, based on the events, is also of great important. Examples along this line include: constructing influence networks among Twitter users from their tweets, soliciting new members to join an event based on their participation history, and recommending positions for job seekers according to their work experience. Following this direction, we study two types of problems - (1) recovering influence networks, and (2) predicting links between a node and a group of nodes, from event sequences.</p></div></div></div>

<p>Security breaches nowadays are not limited to technological orientation. Research in the information security domain is gradually shifting towards human behavioral orientation toward breaches that target weaknesses arising from human behaviors (Workman et al., 2007). Currently, social engineering breaches are more effective than many technical attacks. In fact, the majority of cyber assaults have a social engineering component. Social Engineering is the art of manipulating human flaws towards a malicious objective (Breda et al., 2017). In the likely future, social engineering will be the most predominant attack vector within cyber security (Breda et al., 2017). Human failures, persuasion and social influences are key elements to understand when considering security behaviors. With the increasing concerns for social engineering and advancements in human factors-based technology, phishing emails are becoming more prevalent in exploiting human factors and external factors. Such factors have been researched upon in pairs, not overall. Till date, there is not much research done to identify the collaborative links between authority, urgency, risk perception and human factors such as personality traits, and knowledge. This study investigates about phishing email characters, external influences, human factors influences, and their collaborative effects. </p>

This study examined the level of trust of information on social media. Specifically, I investigated the factors of performance expectancy with information-seeking motives that appear to influence the level of trust of information on various social network sites. This study utilized the following theoretical models: elaboration likelihood model (ELM), the uses and gratifications theory (UGT), the unified theory of acceptance and use of technology model (UTAUT), the consumption value theory (CVT), and the Stimulus-Organism-Response (SOR) Model to build a conceptual research framework for an exploratory study. The research investigated the extent to which information quality and source credibility influence the level of trust of information by visitors to the social network sites. The inductive content analysis on 189 respondents’ responses carefully addressed the proposed research questions and then further developed a comprehensive framework. The findings of this study contribute to the current research stream on information quality, fake news, and IT adoption as they relate to social media.