Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models

The steady growth of graph data from social networks has resulted in wide-spread research in finding solutions to the influence maximization problem. In this paper, we propose a holistic solution to the influence maximization (IM) problem. (1) We introduce an opinion-cum-interaction (OI) model that closely mirrors the real-world scenarios. Under the OI model, we introduce a novel problem of Maximizing the Effective Opinion (MEO) of influenced users. We prove that the MEO problem is NP-hard and cannot be approximated within a constant ratio unless P=NP. (2) We propose a heuristic algorithm OSIM to efficiently solve the MEO problem. To better explain the OSIM heuristic, we first introduce EaSyIM - the opinion-oblivious version of OSIM, a scalable algorithm capable of running within practical compute times on commodity hardware. In addition to serving as a fundamental building block for OSIM, EaSyIM is capable of addressing the scalability aspect - memory consumption and running time, of the IM problem as well. Empirically, our algorithms are capable of maintaining the deviation in the spread always within 5% of the best known methods in the literature. In addition, our experiments show that both OSIM and EaSyIM are effective, efficient, scalable and significantly enhance the ability to analyze real datasets.Comment: ACM SIGMOD Conference 2016, 18 pages, 29 figure

A Gamefied Synthetic Environment for Evaluation of Counter-Disinformation Solutions

This paper presents a simulation-based approach to countering online dis/misinformation. This disruptive technology experiment incorporated a synthetic environment component, based on adapted SIR epidemiological model to evaluate and visualize the effectiveness of suggested solutions to the issue. The participants in the simulation were given a realistic scenario depicting a dis/misinformation threat and were asked to select a number of solutions, described in IoS (Ideas-of-Systems) cards. During the event, the qualitative and quantitative characteristics of the IoS cards, were tested in a synthetic environment (SEN), built after a Susceptible-Infected-Resistant (SIR) model. The participants, divided into teams, presented and justified their dis/misinformation strategy which included three IoS card selections. A jury of subject matter experts, announced the winning team, based on the merits of the proposed strategies and the compatibility of the different cards, grouped together

2022 SDSU Data Science Symposium Program

https://openprairie.sdstate.edu/ds_symposium_programs/1003/thumbnail.jp

A Survey on Visual Analytics of Social Media Data

The unprecedented availability of social media data offers substantial opportunities for data owners, system operators, solution providers, and end users to explore and understand social dynamics. However, the exponential growth in the volume, velocity, and variability of social media data prevents people from fully utilizing such data. Visual analytics, which is an emerging research direction, ha..