Balancing information exposure in social networks
Social media has brought a revolution on how people are consuming news. Beyond the undoubtedly large number of advantages brought by social-media platforms, a point of criticism has been the creation of echo chambers and filter bubbles, caused by social homophily and algorithmic personalization. In this paper we address the problem of balancing the information exposure in a social network. We assume that two opposing campaigns (or viewpoints) are present in the network, and that network nodes have different preferences towards these campaigns. Our goal is to find two sets of nodes to employ in the respective campaigns, so that the overall information exposure for the two campaigns is balanced. We formally define the problem, characterize its hardness, develop approximation algorithms, and present experimental evaluation results. Our model is inspired by the literature on influence maximization, but there are significant differences from the standard model. First, balance of information exposure is modeled by a symmetric difference function, which is neither monotone nor submodular, and thus, not amenable to existing approaches. Second, while previous papers consider a setting with selfish agents and provide bounds on bestresponse strategies (i.e., move of the last player), we consider a setting with a centralized agent and provide bounds for a global objective function.
This paper proposes the problem of exposure balancing in social networks where the independent cascade is used as the diffusion model. The usage of symmetric difference is quite interesting and the approximation scheme to shovel the problem is plausible.
The experiments setup and the way the two cascades are defined and curated are interesting too.
It’s not clear if the two social network campaigns should be opposing or not? Is it a necessity for the model?
Furthermore, some hashtags are not really in opposition to each other.  For example, on obamacare issue, the tag @sentedcurze could represent many many topics related to Senator Ted Cruz including the anti-obamacare.
Any elaboration on the choice of \alpha=0 for the correlated setting.
Given the small cascades size, is the diffusion network learned reliable enough? It looks more like a heuristic algorithm for learning the diffusion network which might be of high variance and less robust given the small cascades size.
In Figure 3 why for some cases the difference between methods is increasing and for some cases it is decreasing?
Using the term “correlated” at the first though reminds the case that the two cascades are competing against each other to attract the audience. This looks to be a better definition of correlation than the one used in the paper. But in this paper, it’s not clear until the user reaches to page 3. I would suggest to clarify this earlier in the paper.
Showing standard deviations on the bar charts of figure 1 would have helped assess the significance of the difference visually. Furthermore, reporting significance levels and p-values could make the results stronger.
A few very related work are missing. Especially paper [1] has formulated the problem of exposure shaping in social networks. Furthermore, paper [2] considers two cascades who are competing against each other and optimizes one of them so they can hit the same number of people, i.e., trying to balance their exposure.  Given papers [1] and [2] I would say the current paper is not the first study of balancing g exposures in social networks. They are closely related.
Paper [3] has proposed a point-process based model for competing cascades in social networks that is related and finally paper [4]  proposes an algorithm to optimize the activities in social networks so that the network reaches to a specific goal.
[1] Farajtabar, Mehrdad, et al. "Multistage campaigning in social networks." Advances in Neural Information Processing Systems. 2016.
[2]  Farajtabar, Mehrdad, et al. "Fake News Mitigation via Point Process Based Intervention." arXiv preprint arXiv:1703.07823 (2017).
[3] Zarezade, Ali, et al. "Correlated Cascades: Compete or Cooperate." AAAI. 2017.
[4] Zarezade, Ali, et al. "Cheshire: An Online Algorithm for Activity Maximization in Social Networks." arXiv preprint arXiv:1703.02059 (2017).