Maximizing Welfare in Social Networks under a Utility Driven Influence Diffusion Model

Motivated by applications such as viral marketing, the problem of influence maximization (IM) has been extensively studied in the literature. The goal is to select a small number of users to adopt an item such that it results in a large cascade of adoptions by others. Existing works have three key limitations. (1) They do not account for economic considerations of a user in buying/adopting items. (2) Most studies on multiple items focus on competition, with complementary items receiving limited attention. (3) For the network owner, maximizing social welfare is important to ensure customer loyalty, which is not addressed in prior work in the IM literature. In this paper, we address all three limitations and propose a novel model called UIC that combines utility-driven item adoption with influence propagation over networks. Focusing on the mutually complementary setting, we formulate the problem of social welfare maximization in this novel setting. We show that while the objective function is neither submodular nor supermodular, surprisingly a simple greedy allocation algorithm achieves a factor of $(1-1/e-\epsilon)$ of the optimum expected social welfare. We develop \textsf{bundleGRD}, a scalable version of this approximation algorithm, and demonstrate, with comprehensive experiments on real and synthetic datasets, that it significantly outperforms all baselines.Comment: 33 page

On terrorist attacks in Nigeria: Stance and engagement in conversations on Nairaland

Terrorist attacks in Nigeria have generated a huge body of conversations and debates on the Internet. This study investigates the contents of these online conversations on Nairaland and how such conversations exhibit stance and civic engagement in response to the attacks. Nairaland is an online community and public space that serves as a meeting place for Nigerians at home and in the Diaspora, who constantly follow-up on the events in Nigeria and participate in political debates about the country. This study argues that the frequent negative evaluations of Boko Haram and the attribution of the activities to Islam and the consistent constructions of northern Nigeria as ‘violent people’ and Islam as an ‘evil’ religion in Nairaland are potential to further worsen religious and ethnic relations in Nigeri

Spaces for interactive engagement or technology for differential academic participation? Google Groups for collaborative learning at a South African University

Published ArticleThe rhetoric on the potential of Web 2.0 technologies to democratize online engagement of students often overlooks the discomforting, differential participation and asymmetrical engagement that accompanies student adoption of emerging technologies. This paper, therefore, constitutes a critical reality check for student adoption of technology to the extent that it explores the potential of Google Groups (i.e. self-organised online groups) to leverage collaborative engagement and balanced participation of students with minimal educator support. Community of Inquiry and a case study approach involving in-depth interviews with racially mixed students and Google Group artifacts were drawn upon as theoretical and methodological lenses for examining the equality of participation, academic rigor and complexity of engagement in Google Groups. Study findings were mixed: a semblance of authentic peer-based engagements, emergent academic networking, and inter-racial communication in Google Groups was juxtaposed with gender asymmetries in participation, dominance of group administrators’ postings and shallow collaborative engagements. The study, therefore, recommends actively engaged Group leaders who steer gender and racially balanced engagements, scaffold peer on-task behavior; including a sound pedagogical strategy anchored in collaborative problem-solving; authentic construction of knowledge; effective completion of collaborative tasks by students; and constructive assessments by the educator and peers

Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks

A typical viral marketing model identifies influential users in a social network to maximize a single product adoption assuming unlimited user attention, campaign budgets, and time. In reality, multiple products need campaigns, users have limited attention, convincing users incurs costs, and advertisers have limited budgets and expect the adoptions to be maximized soon. Facing these user, monetary, and timing constraints, we formulate the problem as a submodular maximization task in a continuous-time diffusion model under the intersection of a matroid and multiple knapsack constraints. We propose a randomized algorithm estimating the user influence in a network ($|\mathcal{V}|$ nodes, $|\mathcal{E}|$ edges) to an accuracy of $\epsilon$ with $n=\mathcal{O}(1/\epsilon^2)$ randomizations and $\tilde{\mathcal{O}}(n|\mathcal{E}|+n|\mathcal{V}|)$ computations. By exploiting the influence estimation algorithm as a subroutine, we develop an adaptive threshold greedy algorithm achieving an approximation factor $k_a/(2+2 k)$ of the optimal when $k_a$ out of the $k$ knapsack constraints are active. Extensive experiments on networks of millions of nodes demonstrate that the proposed algorithms achieve the state-of-the-art in terms of effectiveness and scalability

When One Speaks Out and When One Does Not: Online Discussion Forums for Opinion Expression

Individuals’ opinion expression about public affairs has entered a new phase with the growth of new venues for social interaction among fellow citizens such as online discussion forums. However, not much empirical evidence exists to understand an individual’s voicing views in online discussion. Focusing on this attention-deserved form of political activity online, the current dissertation aimed to yield insights into some fundamental questions: who, with what characteristics, more intends and tends to talk on an online discussion forum, and what forum conditions (and combinations of them) facilitate an individual’s opinion expression intention and behavior. To investigate these questions, two experimental research methods – scenario-based thought and website-based true experiments – were implemented. Thought experiments relied on a hypothetical scenario technique, the most widely used method in spiral of silence research, but employed the multifaceted, detailed scenarios. True experiments, on the other hand, used the stimulus online forums designed for this study to actually place the participants in the online discussion situation. The findings from these two different approaches indicated that a person’s race, issue involvement, issue knowledge, and the revelation of identity were factors that generally influenced opinion expression online. Racial minorities, compared to Whites, were consistently more willing and likely to voice their views on the online forum. Those who were involved in and knowledgeable about the issue under discussion were more likely to post messages to the forum. Disclosing one’s real name and other personal information was a big hindrance to actual opinion expression on the discussion forum. However, comparing the findings from scenarios to those obtained from real, analogous situations also revealed that the use of scenarios could not accurately identify some existing phenomena. Thought and true experiments returned incongruent predictions regarding the roles of age, fear of isolation, and the votes climate as well as the contribution degree of issue knowledge (to posting intention). In particular, trait fear of isolation, which has been pointed out as the primary culprit behind silencing minority opinion holders, played a completely opposite role. Against the background of these findings, the theoretical and methodological implications of the study were discussed.PhDCommunication StudiesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116699/1/ywoh_1.pd