Title: Make Information Diffusion Explainable: LLM-based Causal Framework for Diffusion Prediction
Venue: NeurIPS 2025
Accepted: yes
Keywords: data mining

social networks

information diffusion

large language model
Abstract: Information diffusion prediction, which aims to forecast future infected users during the information spreading process on social platforms, is a challenging and critical task for public opinion analysis. With the development of social platforms, mass communication has become increasingly widespread. However, most existing methods based on GNNs and sequence models mainly focus on structural and temporal patterns in social networks, suffering from spurious diffusion connections and insufficient information for the diffusion analysis. We leverage strong reasoning capability of LLMs and develop a LL**M**-based causal framework for d**i**ffusion inf**l**uence **d**erivation (MILD). Comprehensively integrating four key factors of social diffusion, i.e., connections, active timelines, user profiles, and comments, MILD causally infers authentic diffusion links to construct a diffusion influence graph $G_I$. To validate the quality and reliability of our constructed graph $G_I$, we proposed a newly designed set of evaluation metrics w.r.t. diffusion prediction. We show MILD provides a reliable information diffusion structure that 12% absolutely better than the social network structure and achieves the state-of-the-art performance on diffusion prediction. MILD is expected to contribute to high-quality, more explainable, and more trustworthy public opinion analysis.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes an information diffusion prediction framework (MILD) based on large language models (LLMs). Unlike previous methods that focus only on social structure and propagation sequence, MILD leverages LLMs to infer the causal relationships of “who influences whom” within cascades. It comprehensively integrates multi-dimensional data such as social network structure, user activity, comment content, and temporal information to explicitly construct a directed diffusion influence graph, which is then incorporated into downstream diffusion prediction tasks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths：\n\nIt skillfully integrates social relationships, temporal order, user profiles, and comment content, resulting in comprehensive and well-structured information utilization.\n\nIn addition to ablation studies, the paper also conducts empirical analyses from perspectives such as robustness and attention visualization.\n\nWeaknesses：\n\nMILD relies on large language model inference, which may incur excessive deployment and computational costs in ultra-large-scale social networks or real-time prediction scenarios, posing challenges for practical application.\n\nAlthough LLMs can infer “who influences whom,” the inferred causal chains are difficult to fully verify in real-world scenarios and may introduce subjectivity or hallucinations.\n\nQUESTIONS:\nCan the authors further analyze or provide information on the inference efficiency and resource consumption of MILD when applied to larger-scale, longer cascades, or more complex social networks?\n\nIs there any further external data or human-annotated experiments to validate the causal validity of the “who influences whom” chains inferred by the LLM?\n\nSince the model utilizes sensitive user attributes and comment content, how can privacy and data compliance be ensured in practical applications? Is it possible to anonymize or obfuscate sensitive information?\n\nLIMITATIONS:\nThe authors have discussed some limitations, such as inference cost and the verifiability of LLM outputs, but the analysis of privacy and ethical risks is insufficient. It is recommended to include statements regarding data collection compliance and to further analyze the method's resource consumption and practical applicability in large-scale scenarios.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-25"}

comments: SUMMARY:
This paper proposes a novel multi-step framework to predict information diffusion in social networks, namely, MILD (LLM-based Causal Framework for Diffusion Influence Derivation). One of the novelties of the proposed method is that inlike existing methods that are mainly based on GNNs and sequence models, it uses LLMs to explain underlying influence and infer a "who-influences-whom" diffusion graph. Experiments have been conducted to demonstrate that the proposed framework achieves SOTA accuracy as well as explainable representation of the diffusion mechanism.

STRENGTHS AND WEAKNESSES:
Strengths:
1. Paper is well-written and easy to follow. 
2. LLM used in the method helps with the explainability of the information diffusion process which is at the core of the paper's innovations.
3. The proposed method uses different types of inputs and information which make the method more reliable and stronger.
4. Also extensive experiments (including ablation studies) have been performed to support the proposed idea. Furthermore, experiments have been supported by rigorous theoretical discussions.
5. Extensive evaluations have been performed to show the superiority of the results and the proposed method in general.

Weaknesses:
1. Although this is a nice paper, I have two major concerns regarding (1) computational costs and (2) reproducibility of the results. First of all, the method is comprised of multiple steps that make the method very expensive and the process quite time-consuming. Second, because of LLM and numerous parameters, reproducibility is very challenging due to underlying LLM's reasoning abilities.

QUESTIONS:
I am very curious to see authors' response to my comment that I posed earlier on the weaknesses of the method.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 2
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-30

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a causal framework based on LLMs called MILD to predict information diffusion on social platforms. The framework integrates four key factors (social connections, activity timelines, user profiles, and comments) to infer a diffusion influence graph, which explicitly illustrates the relationships and influences among users involved in information cascades. Unlike traditional models, MILD harnesses the strong reasoning capabilities of LLMs to provide a more accurate and explainable diffusion prediction structure. Experiments show that the graphs constructed by MILD improve the representation of real-world diffusion paths by 12% over traditional social network structures and outperform eight state-of-the-art methods in diffusion prediction performance by approximately 7%.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths  \n1. Innovative causal framework. A novel framework that combines LLMs and multiple factors is proposed to address the issue that traditional methods cannot clearly identify key influential connections.\n  2. Solid theoretical foundation. Provides detailed theoretical analysis and proofs, including complexity analysis of the problem and the theoretical basis for the solution.\n  3. Strong empirical results. Experiments on real-world datasets demonstrate the effectiveness of the method, with performance significantly surpassing existing state-of-the-art approaches.\n## Weaknesses\n  1. Dataset limitations. The experiments mainly focus on two datasets, which may not fully reflect the characteristics of information diffusion on different types of social platforms. Could additional experiments on larger-scale datasets be provided? Furthermore, as far as I know, Weibo is a large-scale dataset with over 1.7 million users, as cited in reference [1]. Why are there only about 30,000 users in the dataset used by the authors? This suggests that the authors performed data reduction, but the paper does not explain this. It is recommended that the authors provide a detailed explanation of how and why the data was reduced.\n  2. High computational complexity. The path search problem in the paper is proven to be NP-hard, and the heuristic-based solution may face computational challenges in large-scale applications. Could the authors provide a comparison of the time costs among various methods or showcase the time cost of MILD on larger-scale datasets?\n  3. Heavy reliance on LLMs. The method is highly dependent on the reasoning ability of LLMs, and different LLMs may produce different results, affecting the stability of the approach. However, the paper only presents results based on GPT-4o and does not compare with other types or versions of LLMs, which may limit the generalizability and applicability of the results. It is suggested to add experimental results using more LLMs.\n\n[1] Social influence locality for modeling retweeting behaviors. IJCAI 2013.\n\nQUESTIONS:\nPlease refer to Strengths and Weaknesses.\n\nLIMITATIONS:\nNo. In fact, Weibo is a large-scale dataset, but it is unclear whether the authors only obtained a subset or performed data reduction. If experiments could be conducted on the complete dataset, it could effectively address the limitations of this work.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the problem of information diffusion prediction. Noting the limitations of prior work based on graph neural networks and sequence models—particularly their lack of interpretability—the authors propose a Large Language Model (LLM)-based causal framework for diffusion influence derivation (MILD). The framework integrates four key factors underlying social diffusion dynamics. Experimental results demonstrate that the proposed approach outperforms traditional methods in both predictive performance and interpretability.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1.\tThe motivation of the paper is sound. Building on the observation that traditional models lack interpretability, the authors propose a causal inference framework enhanced by large language models (LLMs), which is a well-founded approach.\n2.\tThe paper is well-written and easy to follow.\n3.\tThe authors conduct extensive experimental analysis, including comparisons with a wide range of baselines, ablation studies, and empirical evaluations, making the results relatively solid.\n4.\tThe proposed MILD model demonstrates clear improvements over many baseline methods.\n\nWeaknesses:\n1.\tThe core idea of the paper is essentially to leverage LLMs to capture a diffusion influence graph, which is then used for downstream prediction. However, I remain unconvinced about the effectiveness or necessity of the causal inference aspect as claimed. (See Questions)\n2.\tThe discussion around the use of LLMs is somewhat insufficient. It would be beneficial to explore different LLM variants (e.g., open-source models), and to elaborate on the prompt design—such as whether few-shot prompting is necessary.\n3.\tThe paper lacks analysis of important hyperparameters, such as the influence of alpha.\n\nQUESTIONS:\nAbout LLMs: Can the proposed method be adapted to use other open-source LLMs? Additionally, does the prompt design require few-shot examples to achieve good performance?\n\nAbout the causal mechanism: Beyond inspiring the use of LLMs to derive the diffusion influence graph, does the causal framework serve any additional role in the model? How is the do-operator concretely instantiated within the model architecture or inference process?\n\nOn Equation 10: Why do the authors choose to inject the diffusion influence using a +\\alpha A formulation instead of a masked A formulation? What are the advantages or motivations behind this design choice?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}