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Fake News Mitigation via Point Process Based Intervention
Mehrdad Farajtabar, Jiachen Yang, Xiaojing Ye, Huan Xu, Rakshit Trivedi, Elias Khalil, Shuang Li, Le Song, Hongyuan Zha
TL;DR
Fake news mitigation requires intervention in stochastic social-network propagation, while existing responses can involve costly manual oversight and the experiments do not directly test reductions in fake-news events. The paper models fake and mitigation activity with multivariate Hawkes processes, formulates staged exogenous incentives as reinforcement-learning actions, and optimizes them under constraints. Its method outperforms alternatives on synthetic experiments and shows a reasonable margin over competitors in real-time Twitter experiments, with important scope limitations.
Problem
Fake news can spread widely through social media, while direct countermeasures may require costly oversight; the paper seeks an optimization framework for stochastic network intervention.
Method
The paper models fake-news and mitigation activity as networked multivariate Hawkes processes and uses state-dependent exogenous incentives, rewards, and LSTD-based policy optimization.
Results
The method is almost consistently best on synthetic experiments, improving over the random policy by roughly 20 percent, and outperforms three baselines in real-time intervention experiments.
Takeaways & Limitations
Point-process intervention provides a framework for optimizing mitigation objectives based on stochastic fake-news and mitigation exposures under constrained interventions.
Takeaways & Limitations
The real-world experiments do not directly test reduction of fake-news events, and their content-neutral design may not represent semantic effects on propagation.
Abstract
from arXiv · showhide
We propose the first multistage intervention framework that tackles fake news in social networks by combining reinforcement learning with a point process network activity model. The spread of fake news and mitigation events within the network is modeled by a multivariate Hawkes process with additional exogenous control terms. By choosing a feature representation of states, defining mitigation actions and constructing reward functions to measure the effectiveness of mitigation activities, we map the problem of fake news mitigation into the reinforcement learning framework. We develop a policy iteration method unique to the multivariate networked point process, with the goal of optimizing the actions for maximal total reward under budget constraints. Our method shows promising performance in real-time intervention experiments on a Twitter network to mitigate a surrogate fake news campaign, and outperforms alternatives on synthetic datasets.
1. Introduction
The paper frames fake news mitigation as a network intervention problem motivated by widespread social-media exposure and the costs of aggressive, manually overseen responses. It combines multivariate point-process modeling with reinforcement learning to optimize mitigation policies.
- Motivation: Fake news spreads widely on social media, motivating strategies to mitigate its potentially large real-world consequences.The cited passage reports substantial social-media news use, large sharing volumes, and engagement with false stories.
- Motivation: Direct fact-checking and flagging can be costly, slow, vulnerable to abuse, and potentially inconsistent with civil rights.The paper therefore considers optimizing real-news propagation as an alternative strategy.
- Modeling approach: The framework models fake-news and mitigation events as multivariate point processes with self- and mutual excitation and exogenous control.The control is intended to incentivize spontaneous mitigation events, while event exposure counts quantify influence.
- Contributions: The method addresses uncertainty in network activity and propagation by combining Hawkes-process modeling with reinforcement-learning policy optimization.The reinforcement-learning formulation is motivated by intractable exact policy evaluation and iteration problems.
- Contributions: The paper formulates fake-news mitigation as optimal point-process intervention and maps it to an MDP using states, exogenous-intensity actions, and reward functions.Its stated contributions include objectives based on exposure counts, second-order exposure statistics, model-based LSTD, and a real-time intervention experiment.
2. Preliminaries and Problem Statement
The paper represents fake-news and mitigation activity on a social network as coupled multivariate Hawkes processes, measures influence through exposure counts, and controls mitigation through staged exogenous incentives. It then chooses state-dependent interventions to maximize discounted rewards under budget and activity constraints.
- Network activity model: Multivariate Hawkes processes model network events whose past activity can self-excite and mutually excite later events across dimensions.The model uses exogenous intensities and a decaying Hawkes kernel to represent baseline activity and temporal excitation.
- Network activity model: Fake-news and mitigation processes are represented as separate networked point processes, with each user’s event count tracking campaign activity.An event by one connected user can trigger later events at other users.
- Exposure representation: Exposure counts measure how often users encounter campaign events from followed users, while network cascades can transmit exposure along directed paths.The adjacency matrix maps campaign event counts into fake-news and mitigation exposure processes.
- Intervention: At each stage, the intervention adds a nonnegative incentive to selected users’ exogenous mitigation intensities during a fixed time interval.Because incentives increase activity rates rather than specify exact event times, mitigation remains stochastic.
- Optimization problem: The control policy maps states summarizing previous counting-process observations to interventions that maximize discounted rewards under budget and user-activity constraints.The action space can include stage budgets, per-user intensity prices, and caps on user activity.
3. Proposed Method
The method formulates fake-news mitigation as a multistage point-process MDP, using Hawkes-process statistics to evaluate rewards and LSTD policy iteration to optimize interventions. It represents recent fake and mitigation activity in the state, evaluates approximate value functions, and improves policies through one-step look-ahead.
- Reward functions: Two candidate rewards measure mitigation through fake/true-news exposure correlation or unmitigated fake-news counts.The value function discounts stage rewards with γ ∈(0, 1].
- Point-process statistics: First- and second-order Hawkes-process statistics support evaluation of expected rewards and next-state values.The second-order statistics apply to non-stationary multivariate Hawkes processes and enable computation of E[Ni(t)Nj(t′)].
- State representation: The state concatenates exponentially decayed activity summaries and event counts from recent intervals for both fake-news and mitigation processes.With L prior intervals and n dimensions, the feature space uses 2nL activity-count features plus a final feature.
- Policy evaluation: LSTD approximates the value function with linear features by projecting the Bellman update onto the feature space.The fitted coefficients solve the D × D system Aπwπ = bπ, with Aπ = Ψ⊤(Ψ − γΨ′) and bπ = Ψ⊤rπ.
- Policy iteration: Policy improvement uses one-step look-ahead, while LSTD(0) alternates policy evaluation and improvement until the coefficients converge.The real-time procedure selects an action from expected immediate reward plus discounted next-state value, adds it to the exogenous intensity, and generates mitigation events.
4. Experiments
The experiments compare LTD with point-process, structural, exposure-based, and random intervention baselines using theoretical, synthetic, and real-time evaluations. LTD generally performs best, while its advantage varies with network size, campaign size, sparsity, stage length, and objective.
- Evaluation and baselines: The evaluation measures accumulated total reward and compares LTD with CEC, OPL, CLS, EXP, and RND baselines across synthetic and real intervention experiments.The experiments also verify theoretical second-order statistics before evaluating intervention performance.
- Synthetic intervention results: LTD improves over the random policy by roughly 20 percent on synthetic correlation-maximization experiments, with CEC ranking second.OPL, EXP, and CLS follow, with the reported results attributing weaker performance to ignoring state, events, or network structure alone.
- Synthetic intervention results: LTD’s advantage over alternatives increases with network size, while larger mitigation campaigns provide more flexibility for effective intervention.These findings are reported for correlation maximization, with the same patterns observed for distance minimization.
- Synthetic intervention results: As networks become denser, all methods approach random-policy performance because nodes become exposed to mitigation more equally.The authors note that social networks are usually sparse, where the proposed method’s effectiveness is more evident.
- Synthetic intervention results: Longer stage lengths produce higher rewards relative to random intervention and a larger performance gap between LTD and competing methods.The paper reports the same qualitative patterns for the distance-minimization objective.
- Real-time intervention results: In the Twitter experiment, LTD outperforms three baselines, reaches around 7 in difference minimization, and reduces exposure difference to less than 2.6 per user.For correlation maximization, CEC trails LTD by a margin of 5; LTD also makes more mitigation posts during daytime phases.
- Real-time intervention results: Rank-correlation evaluation orders twelve trajectories by objective and by closeness between prescribed intervention and inferred actual intensity.The closeness measure is mean squared error, and the resulting rank correlations compare the five methods’ predictive ordering.
- Linear approximation accuracy: Linear value-function approximation becomes more accurate with more LSTD samples, but its error remains nonzero after 4000 samples.The authors attribute the persistent error to the optimal value function not lying in the linear feature span.
5. Discussion
The point-process intervention framework models uncertainty in event occurrences but cannot determine exact mitigation-event times. Its experiments also use simplifying assumptions and do not directly test reduction of fake-news events.
- The stochastic model sets optimal conditional intensity rather than the precise best times of mitigation events.Shorter campaign stages could improve temporal precision.
- The real-world experiment assumes every event from followed user u is seen by user v.This may overestimate exposure because followers may not access Twitter during the relevant interval.
- Mitigation is defined through selectable event-based objectives rather than necessarily reducing fake-news events.The framework can accommodate objectives based on fake-news and real-news exposures or events.
- The experiments do not directly test whether fake-news events are reduced.The content-neutral real-time experiment also does not fully represent semantic effects on propagation.
A. Proof of Theorem 2
The proof derives second-order event-count relationships for a multivariate Hawkes process and establishes the unique determination of its covariance-related matrix solution.
- The proof concludes after combining the moment equations and the matrix relation.
- The matrix G(t′, t) collects the functions g_ji(t′, t) as its entries.This matrix representation lets the componentwise equations be combined compactly.
- The Wiener-Hopf equation uniquely determines G(t′, t) for t ≥ t′.Symmetry under exchanging indices and times determines the remaining case t < t′.
- The proof expresses second-order event-count moments using the covariance-related function g_ji and conditional intensities.The derivation combines equations for ordered pairs of dimensions and times.
B. Details of Policy Evaluation
Policy evaluation approximates the value function with linear features and obtains the coefficients by solving a D-dimensional linear system.
- The approximate value function is required to remain invariant under one Bellman-operator application followed by orthogonal projection.
- Replacing the value function with its linear approximation yields a projected policy-evaluation system.
- The estimated coefficients solve A^πw^π = b^π, where A^π = Ψ^⊤(Ψ − γΨ′) and b^π = Ψ^⊤r^π.The system has D unknown coefficients.
C. Details of Policy Improvement
Policy improvement estimates expected next-state values and rewards from Hawkes-process moments, then optimizes intervention objectives for correlation maximization or difference minimization.
- Expected next-state values account for L historical intervals, with only the most recent interval re-evaluated under uncertainty.
- Correlation maximization rewards exposing users who see fake news to corresponding mitigation news.
- The intervention objective is linear in the control, and linear constraints make the resulting optimization problem convex.
- Difference minimization is treated as a separate reward objective in policy improvement.
- The policy-improvement procedure computes first- and second-order moments before evaluating expected rewards.