Paper:
Learning and Forecasting Opinion Dynamics in Social Networks
Social media and social networking sites have become a global pinboard for exposition and discussion of news, topics, and ideas, where social media users often update their opinions about a particular topic by learning from the opinions shared by their friends. In this context, can we learn a data-driven model of opinion dynamics that is able to accurately forecast users' opinions? In this paper, we introduce SLANT, a probabilistic modeling framework of opinion dynamics, which represents users' opinions over time by means of marked jump diffusion stochastic differential equations, and allows for efficient model simulation and parameter estimation from historical fine grained event data. We then leverage our framework to derive a set of efficient predictive formulas for opinion forecasting and identify conditions under which opinions converge to a steady state. Experiments on data gathered from Twitter show that our model provides a good fit to the data and our formulas achieve more accurate forecasting than alternatives.

Review:
In a nutshell, the paper proposes a new model for opinion-dynamics in social networks (where users can publish             messages/posts/tweets). The authors discuss how their model can be efficiently trained, used to predict opinion             dynamics. The experiments demonstrate that the model has more predictive power compared to existing alternatives.              In my view, the following aspects of the model make it stand out among the classical models for opinion dynamics:             1) The opinions of individuals are updated asynchronously. That is, only the opinion of those who observe a new             post/message gets updated.             2) The model captures the intentisity of the posted messages which highly impacts how the opinions will develop. On the             other hand, most classical models assume that opinions get updated at a constant rate.             3) The model assumes that the observed signals (posted messages) are simply noisy reflections of the underlying opinions.  This makes the model more robust and applicable when dealing with real-world datasets.              The technical aspects of the paper is mostly concerned with how the proposed model can be viewed as Markov process and how it can be expressed using SDEs. Furthermore, the paper discusses how the model can be trained using existing methods             and how it can be simulated. A simulation, in this case, is essentially equivalent to sampling from the proposed generative             model which is not trivial, but the authors point out how it can be done using existing algorithms.              Finally, the experiments highlight that (a) the model is complex to produce exteremly different outcomes (i.e, consensus vs. polarization), and (b) the model has a higher predictive power compared to existing opinion-dyanmic models. The real-world             experiments are performed on datasets obtained from twitter.
          The technical aspects of the paper (i.e., the genarative model, how it can be learned, and how it can be simulated) are quite             interesting. The proposed model is a complex data-oriented model (compared to its alternatives) which I expected to be too difficult to study analytically. Nevertheless, the paper talks about both simulation-based and analytical forcasting using the proposed model.  As I mentioned earlier, the proposed model has some unique properties that makes it interesting compared to classical models for opinion-dynamics.              While I really enjoyed the technical contributions of the paper, I feel the experiments don't do justice to the paper. For instance,             1) the predictive power of the model is only tested on twitter.             2) two (decay) parameters of the model are determined through cross-validation. Is this search easy? What is the appropriate range             and granularity for these parameters? Is there a smart way to search this space?             3) It is not really clear how much data is used for training. This is only reported in percentages, but I'm curious to know             how many tweets were available when an opinion is forecasted (say in Figure 4).              Overall, I think the paper is well-written with significant technical contributions. To me, the experiments can be enriched to further improve the quality of the paper.