The influence estimation and maximization problems study the expected reach of a seed set in social networks under a stochastic propagation model. Motivated by the practical utility of characterizing the distribution of reach values, we systematically analyze the tail behaviour of the reach of a seed set. We study tail bound query problems that, for a given seed set, compute either the maximum reach for a given probability threshold or the highest probability of achieving a target reach. We prove #P-hardness and propose algorithms that balance efficiency and accuracy. We also examine tail bound optimization problems that find a seed set maximizing reach for a target probability or maximizing the probability of achieving a target reach, and establish strong inapproximability results.

Abstract We consider misinformation propagating through a social network and study the problem of its prevention. The goal is to identify a set of $k$ users that need to be convinced to adopt a limiting campaign so as to minimize the number of people that end up adopting the misinformation. This work presents Reverse Prevention Sampling (RPS), an algorithm that provides a scalable solution to the misinformation mitigation problem. Our theoretical analysis shows that RPS runs in $O((k + l)(n + m)(\frac{1}{1 - \gamma }) \log n / \epsilon ^2 )$ expected time and returns a $(1 - 1/e - \epsilon )$-approximate solution with at least $1 - n^{-l}$ probability (where $\gamma $ is a typically small network parameter and $l$ is a confidence parameter). The time complexity of RPS substantially improves upon the previously best-known algorithms that run in time $\Omega (m n k \cdot POLY(\epsilon ^{-1}))$. We experimentally evaluate RPS on large datasets and show that it outperforms the state-of-the-art solution by several orders of magnitude in terms of running time. This demonstrates that misinformation mitigation can be made practical while still offering strong theoretical guarantees.

We propose an information propagation model that captures important temporal aspects that have been well observed in the dynamics of fake news diffusion, in contrast with the diffusion of truth. The model accounts for differential propagation rates of truth and misinformation and for user reaction times. We study a time-sensitive variant of the misinformation mitigation problem, where k seeds are to be selected to activate a truth campaign so as to minimize the number of users that adopt misinformation propagating through a social network. We show that the resulting objective is non-submodular and employ a sandwiching technique by defining submodular upper and lower bounding functions, providing data-dependent guarantees. In order to enable the use of a reverse sampling framework, we introduce a weighted version of reverse reachability sets that captures the associated differential propagation rates and establish a key equivalence between weighted set coverage probabilities and mitigation with respect to the sandwiching functions. Further, we propose an offline reverse sampling framework that provides (1 - 1/ e - ϵ)-approximate solutions to our bounding functions and introduce an importance sampling technique to reduce the sample complexity of our solution. Finally, we show how our framework can provide an anytime solution to the problem. Experiments over five datasets show that our approach outperforms previous approaches and is robust to uncertainty in the model parameters.

Social media platforms provide valuable and powerful means with which users can share content, comment, and communicate. They also suffer from abuse through the dissemination of fake news and misinformation. While a fair amount of work has been done on detecting fake news, on the complementary problem of limiting its propagation, progress has been modest. Once an item is detected as fake, a social media company can intervene on the item and take an appropriate action, including hard intervention (e.g., removing an account) and soft intervention (e.g., labeling the item as "suspicious"). Given that fake news detectors are not 100% reliable, we study the problem of developing a cost aware intervention policy which decides whether to intervene based on the truthiness and popularity of the item. Our solution, Solomon, consists of three modular components - truthiness estimation, popularity estimation (with and without intervention), and intervention policy. Our extensive experiments on real and fake news from multiple domains show that Solomon can perform effective intervention.

Motivated by applications such as question answering, fact checking, and data integration, there is significant interest in constructing knowledge graphs by extracting information from unstructured information sources, particularly text documents.Knowledge graphs have emerged as a standard for structured knowledge representation, whereby entities and their inter-relations are represented and conveniently stored as (subject, predicate, object) triples in a graph that can be used to power various downstream applications.The proliferation of financial news sources reporting on companies, markets, currencies, and stocks presents an opportunity for extracting valuable knowledge about this crucial domain.In this paper, we focus on constructing a knowledge graph automatically by information extraction from a large corpus of financial news articles.For that purpose, we develop a high precision knowledge extraction pipeline tailored for the financial domain.This pipeline combines multiple information extraction techniques with a financial dictionary that we built, all working together to produce over 342,000 compact extractions from over 288,000 financial news articles, with a precision of 78% at the top-100 extractions.The extracted triples are stored in a knowledge graph making them readily available for use in downstream applications.

In this work, we consider misinformation propagating through a social network and study the problem of its prevention. In this problem, a "bad" campaign starts propagating from a set of seed nodes in the network and we use the notion of a limiting (or "good") campaign to counteract the effect of misinformation. The goal is to identify a set of k users that need to be convinced to adopt the limiting campaign so as to minimize the number of people that adopt the "bad" campaign at the end of both propagation processes. This work presents RPS (Reverse Prevention Sampling), an algorithm that provides a scalable solution to the misinformation prevention problem. Our theoretical analysis shows that RPS runs in O((k + l)(n + m)(1/(1 - γ)) log n / ε²) expected time and returns a (1 - 1/e - ε)-approximate solution with at least 1 - n^{-l} probability (where γ is a typically small network parameter and l is a confidence parameter). The time complexity of RPS substantially improves upon the previously best-known algorithms that run in time Ω(m n k ⋅ POLY(ε^{-1})). We experimentally evaluate RPS on large datasets and show that it outperforms the state-of-the-art solution by several orders of magnitude in terms of running time. This demonstrates that misinformation prevention can be made practical while still offering strong theoretical guarantees.

Fake news is a major threat to global democracy resulting in diminished trust in government, journalism and civil society. The public popularity of social media and social networks has caused a contagion of fake news where conspiracy theories, disinformation and extreme views flourish. Detection and mitigation of fake news is one of the fundamental problems of our times and has attracted widespread attention. While fact checking websites such as snopes, politifact and major companies such as Google, Facebook, and Twitter have taken preliminary steps towards addressing fake news, much more remains to be done. As an interdisciplinary topic, various facets of fake news have been studied by communities as diverse as machine learning, databases, journalism, political science and many more. The objective of this tutorial is two-fold. First, we wish to familiarize the database community with the efforts by other communities on combating fake news. We provide a panoramic view of the state-of-the-art of research on various aspects including detection, propagation, mitigation, and intervention of fake news. Next, we provide a concise and intuitive summary of prior research by the database community and discuss how it could be used to counteract fake news. The tutorial covers research from areas such as data integration, truth discovery and fusion, probabilistic databases, knowledge graphs and crowdsourcing from the lens of fake news. Effective tools for addressing fake news could only be built by leveraging the synergistic relationship between database and other research communities. We hope that our tutorial provides an impetus towards such synthesis of ideas and the creation of new ones.

The minimum feedback arc set problem is an NP-hard problem on graphs that seeks a minimum set of arcs which, when removed from the graph, leave it acyclic. In this work, we investigate several approximations for computing a minimum feedback arc set with the goal of comparing the quality of the solutions and the running times. Our investigation is motivated by applications in Social Network Analysis such as misinformation removal and label propagation. We present careful algorithmic engineering for multiple algorithms to improve the scalability of each approach. In particular, two approaches we optimize (one greedy and one randomized) provide a nice balance between feedback arc set size and running time complexity. We experimentally compare the performance of a wide range of algorithms on a broad selection of large online networks including Twitter, LiveJournal, and the Clueweb12 dataset. The experiments reveal that our greedy and randomized implementations outperform the other approaches by simultaneously computing a feedback arc set of competitive size and scaling to web-scale graphs with billions of vertices and tens of billions of arcs. Finally, we extend the algorithms considered to the probabilistic case in which arcs are realized with some fixed probability and provide detailed experimental comparisons.

In this work, we study the problem of clearing contamination spreading through a large network where we model the problem as a graph searching game. The problem can be summarized as constructing a search strategy that will leave the graph clear of any contamination at the end of the searching process in as few steps as possible. We show that this problem is NP-hard even on directed acyclic graphs and provide an efficient approximation algorithm. We experimentally observe the performance of our approximation algorithm in relation to the lower bound on several large online networks including Slashdot, Epinions, and Twitter.

In this work, we study the problem of clearing contamination spreading through a large network where we model the problem as a graph searching game. The problem can be summarized as constructing a search strategy that will leave the graph clear of any contamination at the end of the searching process in as few steps as possible. We introduce an efficient algorithm and experimentally observe its performance on several large online networks including Slashdot, Epinions and Twitter.