Mitigating Social Hazards: Early Detection of Fake News via Diffusion-Guided Propagation Path Generation

Authors: transformer  encoder
Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-04-06 05:36:03

Abstract: The detection of fake news has emerged as a pressing issue in the era of online social media. To detect meticulously fabricated fake news, propagation paths are introduced to provide nuanced social context to complement the pure semantics within news content. However, existing propagation-enhanced models face a dilemma between detection efficacy and social hazard. In this paper, we investigate the novel problem of early fake news detection via propagation path generation, capable of enjoying the merits of rich social context within propagation paths while alleviating potential social hazards. In contrast to previous discriminative detection models, we further propose a novel generative model, DGA-Fake, by simulating realistic propagation paths based on news content before actual spreading. A guided diffusion module is integrated into DGA-Fake to generate simulated user interaction sequences, guided by historical interactions and news content. Evaluation across three datasets demonstrates the superiority of our proposal. Our code is publicly available in https://anonymous.4open.science/r/DGA-Fake-1D5F/.

Main paper: 
 introduction: 
 Fake news detection has garnered significant attention in recent years, driven by the rapid development of online social media platforms [14] . The widespread and rapid dissemination of toxic fake news poses a potentially immeasurable social hazard to both users and society. Traditional fake news detection methods [4, 18, 22, 26] generally focus on capturing the semantics within the news content via deep modeling architectures as illustrated in Figure 1 (a) . In order to evade detection effectively [29] , recent fake news is often meticulously fabricated by skilled malicious publishers, resulting in their indistinguishability for detection models that solely rely on semantic patterns [11] .In efforts to bolster detection capabilities, the propagation path is introduced as a complement to pure semantics [34, 41] . The propagation path encompasses user interactions during the dissemination of news on social networks [18] , reflecting temporal and structural features of news propagation. Such propagation paths are capable of providing extra social context to facilitate the detection of fake news. For example, the propagation paths of fake news tend to be wider, deeper, and contain more social bots than real news [31] , which is a significant clue to verify the fake news.While the integration of propagation paths enhances the detection of well-fabricated news, such methods inherently face the dilemma between detection efficacy and social hazard. The abundant social context within extensive propagation paths is crucial for successful detection. On the other side of the coin, sufficient propagation paths also indicate that the fake news has already achieved widespread dissemination, leading to substantial social hazards, as depicted in Figure 1 (b) . Detecting fake news that has already spread widely and caused significant social damage is somewhat futile. To mitigate this dilemma and identify fake news at an early stage, various forms of social context information have been introduced, such as user profiling [12, 32] , publisher credibility assessment [42] , and user comments analysis [24] . However, the acquisition of additional auxiliary information is time-consuming and labor-intensive, limiting its applicability in data-sparse scenarios. Moreover, the global structures and temporal features within the limited propagation paths remain insufficient.The incorporation of propagation paths enhances detection performance, albeit accompanied by associated social hazard stemming from the spread of fake news. This brings us a question: Can we leverage the rich social context within the propagation paths to enhance the early-stage fake news detection? Different from existing discriminative models, our motivation lies in predicting and generating the forthcoming propagation paths based on the multimodal content and propagation patterns gleaned from previous news. As illustrated in Figure 1 (c), our objective is to discern the patterns beneath the news propagation process, which enables the simulation of propagation paths, thereby facilitating the early detection of emerging news. This generation-based strategy does not necessitate additional social context and is anticipated to yield rich structural and temporal propagation features.However, designing generative models for propagation path generation is non-trivial due to the two reasons. Firstly, it is challenging for generative models to accurately represent the distribution of discrete propagation paths. For example, the GAN-based models often encounter instability issues [3, 16] , while the VAE-based models are susceptible to posterior collapse [35, 43] . Secondly, the diversity and complex connections inherent in user interactions within propagation paths pose a challenge for traditional generative models due to their limited capacity for diverse representation [40] .Based on such desiderata, we resort to denoising diffusion models [9] as solutions, which model discrete user interactions to generate diverse and high-quality propagation paths. However, directly applying vanilla denoising diffusion models to the studied task is undesirable. Firstly, the denoising process lacks controllable conditions. It is non-trivial to regulate the generation process to generate reasonable and informative propagation paths. Secondly, it is challenging to integrate multi-condition features and incorporate them into the generation process. Finally, modeling structural and temporal features from propagation paths simultaneously while enhancing detection performance poses significant challenges.In this paper, we propose a novel Diffusion Guided Propagation Augmentation Fake News Detection (DGA-Fake) model for multimodal fake news detection. Diverging from the prevailing discriminative detection methods, we propose simulating realistic propagation paths based on news content at the early detection stage. The simulation of user interaction paths is facilitated by a novel guided diffusion module. This module aids in stepwise denoising to produce reasonable next interaction user embeddings from randomly sampled Gaussian noise. Specifically, the denoising phase is guided by historical user interaction paths and news content, rendering it autoregressive in generating long propagation paths. Moreover, we propose a propagation path enhanced detection module, which focuses on the user sequence hypergraph to learn temporal depth information and the propagation directed graph to model propagation global structures. DGA-Fake is extensively evaluated over three datasets, and the experimental results demonstrate its superiority.Our contributions are summarized as follows:• To the best of our knowledge, we are the first to investigate the novel problem of early fake news detection utilizing propagation path generation, enjoying the merits of rich social context while alleviating potential social hazards. , where 𝑦 = 0 denotes fake news and 1 stands for real news. Definition 2.2 (Fake News Early Detection). At the early detection stage, the existing propagation paths are limited and may even be nonexistent. Specifically, the sequence 𝑆 𝑖 = {𝑛 0 } signifies a lack of user interactions for the news item 𝑛. We aim to generate propagation paths and effectively discriminate fabricated news in the early stages, which is formalized as follows:EQUATIONwhere P ′ is the simulated propagation paths base on news item 𝑛 and historical propagation path P. 
diffusion model: 
 In this section, we will introduce the fundamental concept of the diffusion model [9] .The forward process of diffusion model is a Markov Chain that gradually adds Gaussian noise to x 0 :EQUATIONwhere 𝑇 is the number of steps to add noise and [𝛽 1 , 𝛽 2 , . . . , 𝛽 𝑇 ] are the variance schedule. It is difficult to learn 𝛽 𝑇 directly, so a reparameterization trick [15] is adopted and transforms the forward process as follows:EQUATIONwhere 𝛼 𝑡 = 1 -𝛽 𝑡 , ᾱ𝑡 = 𝑡 𝑠=1 𝛼 𝑠 and 𝝐 ∼ N (0, I). When ᾱ𝑇 ≈ 0, x 𝑇 is almost Gaussian in distribution, so we have 𝑞(x 𝑇 ) ≈ N (x 𝑇 ; 0, I).Process. The reverse process aims to recover the original data sample x 0 from the completely noisy x 𝑇 ∼ N (0, I). The reverse process is formulated as:𝑝 𝜃 x 0 , • • • , x 𝑇 = 𝑝 x 𝑇 𝑇 𝑡 =1 𝑝 𝜃 x 𝑡 -1 | x 𝑡 , 𝑝 𝜃 x 𝑡 -1 | x 𝑡 = N x 𝑡 -1 ; 𝝁 𝜃 x 𝑡 , 𝑡 , 𝚺 𝜃 x 𝑡 , 𝑡 ,where 𝝁 𝜃 x 𝑡 , 𝑡 and 𝚺 𝜃 x 𝑡 , 𝑡 are the mean and the variance parameterized by 𝜃 .Step Embedding Table Step Embedding Table … 𝛽 𝑇 
methodology: 
 Previous early detection models often prioritize integrating additional social context as a supplement to insufficient propagation paths. However, such approaches are limited by their reliance on time-consuming and labor-intensive auxiliary information, particularly in data-sparse scenarios. In this paper, we propose a method for generating propagation paths in the early stages of news dissemination. To enhance detection capabilities while mitigating social hazards, we introduce a novel approach called Diffusion Guided Propagation Augmentation for Fake News Detection (DGA-Fake) in Figure 2 . DGA-Fake generates propagation sequences based on content features and historical interactions. Following the diffusion guided propagation generation, we further propose a propagation path enhanced detection module (depicted in Figure 4 ) to integrate propagation global structure and temporal sequence information and detect the fake news at early stage.At the early stage of news dissemination, the user interaction is insufficient to provide extra social context, thereby limiting the efficacy of detection performance. Consequently, we propose a propagation path generator to simulate user interaction by a guided diffusion module at data sparse situation. In this section, we first introduce the dual conditional denoiser, a module to integrate control signals into diffusion, as illustrated in Figure 3 . Subsequently, we describe the training phase (Section 3.1.2) and generation phase (Section 3.1.3) based on denoising diffusion model.The vanilla denoising diffusion probabilistic model is not feasible to generate reasonable propagation paths. This is primarily because the generation process lacks controllable conditions from news content and historical sequences.To address this issue, we design a dual conditional denoiser module to integrate the multi-condition features and provide guidance for propagation path generation, spanning from random noise to user embedding. Firstly, we employ the Transformer encoder to encode the sequential path, c 𝑚-1 = Transformer(𝑛 0 , 𝑢 1 , • • • , 𝑢 𝑚-1 ), where the c 𝑚-1 denotes the embedding of the historical sequence.For the news content input, we embed the textual and visual content as r 𝑚-1 = [n 𝑇 ||n 𝑉 ], where || is the concatenation operation. The one-denoising step guided with dual conditions is as follows:û𝑡 𝑚 =LN(SelfAtt([u t m ||𝛽 t ]) + [u t m ||𝛽 t ]), ĉ𝑚-1 =LN(CrossAtt(ĉ m-1 , ût m ) + ût m ), r𝑚-1 =LN(CrossAtt(r m-1 , ĉm-1 ) + ĉm-1 ), u 𝑡 -1 𝑚 =LN(FF(r m-1 ) + ût m )),where SelfAtt is the self-attention, CrossAtt is the cross-attention, LN is the layer norm and FF is the feed-forward layer. The user embedding u 𝑡 𝑚 and step embedding 𝛽 t are concatenated firstly. Then historical sequence embedding r 𝑚-1 and news content embedding c 𝑚-1 , are integrated by CrossAtt respectively. The u 𝑡 -1 𝑚 is the denoising user embedding from step 𝑡 to step 𝑡 -1. We integrate the dual conditions features into noise embedding through crossattention operation and control the denoising process to generate reasonable user embedding.Phase. In the training phase, rather than applying denoising process from uncontrollable, we reconstruct it with dual conditional denoiser to incorporate multiconditions. With the guide of news content and sequential path, the denoising process is as follows: EQUATION𝜃 = 𝜃 -𝜇∇ 𝜃 𝜖 -𝑓 𝜃 (u 𝑡 𝑚 , c 𝑚-1 , r 𝑚-1 , 𝑡 ) 2 ;10 endwhere the architecture of 𝒇 𝜃 (u 𝑡 𝑚 , c 𝑚-1 , r 𝑚-1 , 𝑡) is the Dual Conditional Denoiser, 𝑡 is the time step.Similar to Eq. 2, the forward diffusion is formulated as a Markov chain of Gaussian transitions:EQUATIONwhere the [𝛽 1 , 𝛽 2 , . . . , 𝛽 𝑇 ] is the variance schedule.The objective of propagation path generation is to optimize the variational bound of negative log-likelihood, which equals minimizing the KL-divergence between 𝑞 u 0:𝑇 𝑚 and 𝑝 𝜃 u 0:𝑇 𝑚 :E -log 𝑝 𝜃 (u 0 𝑚 ) ≤ 𝐷 𝐾𝐿 𝑞 u 0 𝑚 , u 1 𝑚 , • • • , u 𝑇 𝑚 ∥𝑝 𝜃 u 0 𝑚 , u 1 𝑚 , • • • , u 𝑇 𝑚 = 𝑇 ∑︁ 𝑡 =1 𝐷 𝐾𝐿 𝑞(u 𝑡 -1 𝑚 |u 𝑡 𝑚 , u 0 𝑚 )||𝑝 𝜃 (u 𝑡 -1 𝑚 |u 𝑡 𝑚 ) + 𝐶,where 𝐶 is the constant. For the simplified objective, we randomly sample a fix number of 𝑡 and minimize the KL-divergence in each iteration, which can be expressed as follows:EQUATION.More detail about the optimization is displayed in Appendix A.1.The algorithm for conditional diffusion training is presented in Algorithm 1. The procedure starts with encoding the historical sequence c 𝑚-1 and news content r 𝑚-1 in lines 1-5. Next, the Gaussian noise is generated from the sampled step 𝑡 and the target user embedding is diffused by noise in lines 6-8. Finally, the parameter 𝜃 in 𝒇 𝜃 is optimized by gradient descent in line 9.Phase. In the generation phase, we target to generate reasonable and informative propagation paths, given the news content and historical sequence. Inspired by [6] , we jointly introduce an additional unconditional model using classifierfree guidance scheme [10] . Specially, the guidance conditions c 𝑚-1 and r 𝑚-1 are replaced randomly by a dummy token Φ with the probability 𝜆 to represent as the unconditional diffusion model. To manipulate the influence of the guidance conditions, the 𝒇 𝜃 is modified as the following format:EQUATIONf𝜃 (u 𝑡 𝑚 , c 𝑚-1 , r 𝑚-1 , 𝑡) = (1 + 𝑤) 𝒇 𝜃 (u 𝑡 𝑚 , c 𝑚-1 , r 𝑚-1 , 𝑡) -𝑤 𝒇 𝜃 (u 𝑡 𝑚 , Φ, 𝑡),where 𝑤 is a hyperparameter to control the influence of conditions. A higher 𝑤 can enhance conditional guidance, but it could potentially undermine diffusion generalization, consequently deteriorating the quality of the generated user embeddings. The onedenoising step process is as follows:EQUATIONwhere z ∼ N (0, I). After the propagation generation phase, the user embedding u 0 𝑚 is generated by denoising the Gaussian noise sample u 𝑇 𝑚 ∼ N (0, I) for 𝑇 steps. We further simplify the above conditional diffusion generation process asu 0 𝑚 = Diff-GEN(𝑛, {𝑛 0 , 𝑢 1 , 𝑢 2 , • • • , 𝑢 𝑚-1 }), where u 0 𝑚 is the generated next user embedding. To identify the particular user within the user set, we utilize an inner product measurement for retrieving the K-nearest users from the candidate set. This process results in the generation of K new sequential paths. Subsequently, the propagation path set P is updated by incorporating the newly generated sequential paths. And then we randomly sample the candidate user sequence path as 𝑆 𝑖+1 = {𝑛 0 , 𝑢 1 , • • • , 𝑢 |𝑆 𝑖+1 | }, where |𝑆 𝑖+1 | denotes the sequence length, and the Diff-GEN(𝑛, 𝑆 𝑖+1 ) is applied autoregressively to generate the next user embedding 𝑢 |𝑆 𝑖+1 |+1 . This generation process is iteratively repeated, resulting in the refined news propagation path set P ′ .The algorithm for propagation generation is in Algorithm 2. The procedure starts with sampling the sequence and encoding conditions in lines 1-5. Next, denoising steps are taken for 𝑇 times in lines 6-9. Finally, the u 0 𝑚 is retrieved with Top-K user embeddings and the set P is updated in line 10. After the augmentation propagation paths are generated by denoising phase, we target to learn the propagation global structure and temporal depth features from the sufficient simulation propagation information. Here, we introduce a propagation path enhanced detection module (depicted in Figure 4 ), which models the propagation path set P ′ into the directed graph and sequence hypergraph to capture the structure and temporal features respectively. Formally, each hyperedge 𝑒 𝑖 ∈ 𝐸 𝐻 connects all users that in the same sequence 𝑆 𝑖 = {𝑢 0 , 𝑢 1 , . . . , 𝑢 |𝑆 𝑖 | }. After modeling the sequential path into a hyperedge, we use a hypergraph attention network (Hyper-GAT) with two stage aggregation to learn the temporal depth information [7] .Node-to-edge Attention. The hyperedge e i is represented by aggregating all connected nodes:𝛼 𝑖𝑘 = exp(a T 1 û𝑘 ) 𝑢 𝑝 ∈𝑒 𝑗 exp(a T 1 û𝑝 ) , û𝑘 = ReLU(W 1 u 𝑙 -1 𝑘 ), e 𝑙 𝑖 =𝜎 ∑︁ 𝑢 𝑘 ∈𝑒 𝑖 𝛼 𝑖𝑘 W 1 u 𝑙 -1 𝑘 ,where 𝜎 is the activation function, W 1 is trainable weight matrix, a 1 is the weight vector. 𝛼 𝑖𝑘 is the attention coefficient, 𝑙 is the layer of Hyper-GAT.Edge-to-node Attention. Then we integrate all hyperedges E 𝑖 participated by user 𝑢 𝑖 to update the user node representation:𝛽 𝑖 𝑗 = exp(a T 2 ê𝑗 ) 𝑒 𝑝 ∈ E 𝑖 exp(a T 2 ê𝑝 ) , ê𝑗 = ReLU([W 2 e 𝑙 𝑗 ||W 3 u 𝑙 -1 𝑖 ]), u 𝑙 𝑖 =𝜎 ∑︁ 𝑒 𝑗 ∈ E 𝑖 𝛽 𝑖 𝑗 W 2 e 𝑙 𝑗 ,where a 2 is weight vector. W 2 and W 3 are trainable weight matrix.To learn the global propagation structure, a directed propagation graph is constructed from propagation paths, which is denoted as 𝐺 = (𝑈 𝐺 , 𝐸 𝐺 ). Each edge 𝑒 𝑖 ∈ 𝐸 𝐺 connects two users in a single sequence. Graph attention network [37] is applied to learn global structure features:𝛼 𝑖𝑘 = exp(a T 3 û𝑘 ) 𝑘 ∈ N (𝑖 ) exp(a T 3 û𝑘 ) , û𝑘 = ReLU(W 4 u 𝑙 -1 𝑘 ||W 4 u 𝑙 -1 𝑖 ) u 𝑙 𝑖 =𝜎 ∑︁ 𝑢 𝑘 ∈ N 𝑖 𝛼 𝑖𝑘 W 4 u 𝑙 -1 𝑘 ,where a 3 is the weight vector and W 4 is the trainable weight matrix. and fed into a prediction layer to make the final decision:u 𝐻 = readout(U 𝐻 ), u 𝐺 = readout(U 𝐺 ), P(𝑦 | 𝑛, 𝐺, H ) = sigmod(W 𝑃 [u 𝐻 ∥u 𝐺 ∥r] + b),where u H is pooled graph level feature as temporal depth feature and u G is the global structure feature. The readout is applied by mean pool. Finally, the cross-entropy loss is calculated as:EQUATION 
experiment 4.1 experimental settings: 
 Datasets. We conduct experiments on three datasets: PolitiFact, GossipCop [30] , and Pheme [25] , which contain 484, 5,526 and 3,506 news, respectively. The dataset is randomly split into training, validation, and testing sets with a ratio of 7:1:2 [38] . More dataset statistics are presented in the Appendix A.2.Baselines. Two types of SOTA baselines are adopted, including content-based: BERT [5] , MVAE [14] , EANN [38] , CAFE [4] , LLAMA2 [36] and Detect-GPT [2] . Propagation-enhanced: CSI [28] , Bi-GCN [1] , UPFD [8] and MFAN [44] . More detailed descriptions about baselines are listed in the Appendix A.3. Implementation Details. We employ AdamW [20] as the optimizer, and the batch size is set at 16. The hidden size of user embedding is set to 128. More details about the implementation and hyper-parameter are demonstrated in Appendix A.4.Each detection model is executed five times, and the average results are reported in Table 1 and  3 : Ablation studies of DGA-Fake on three datasets.the detection of fake news. The DGA-Fake achieves the competitive results with propagation-enhanced baselines. Specially, our proposal outperforms all baselines on PolitiFact dataset. It demonstrates that generated propagation paths are capable of providing more context information even in the data sufficient scenarios.In order to evaluate the detection capabilities of DGA-Fake at early propagation stage, we conduct the comparison with early detection baselines in Table 2 . To simulate early detection scenarios in the test dataset, we remain the early user engagements in the range of 3 to 5 randomly as the accessible propagation paths. One can clearly see that the performances of propagation-enhanced methods decline a lot and even perform worse than some content-based methods (CAFE). This indicates insufficient propagation paths are difficult to capture the complex global structure and temporal sequence information. Besides, we further compare with content-based large language model baselines LLAMA2 [36] and Detect-GPT [2] , which is deployed through the zero-shot prompt engineering. Notably, the LLM-based approaches performs worse than propagation-enhanced methods (in Table 1 ) and our DGA-Fake. It indicates that the textual features alone prove insufficient in adequately verifying the veracity of news. After introducing generated propagation paths by DGA-Fake, detection performance is further improved, revealing the pivotal role of sufficient propagation paths. Specially, DGA-Fake consistently outperforms all baselines over three datasets, achieving +4.2% on PolitiFact, +3.5% on GossipCop, and +1.9% on Pheme in terms of accuracy. Our proposal generates propagation paths based on news content, causing low social hazard while outperforming propagation-enhanced methods in early stage detection. The ablation studies are conducted to investigate the importance of different modules in Table 3 .Conditional Diffusion. We aim to study the influence of different conditions. "w/o news content", "w/o user history", and "w/o all conditions" stand for without news content, user sequence, and both. (1) The performance declines more with "w/o news content", revealing that news content is more important than user history. (2) Without all conditions, performance declines significantly, proving both conditions are crucial to improve performance.Dual Conditional Denoiser. Here we focus on investigating the effectiveness of dual conditional denoiser. "cross-attention" and "concat+MLP" denote to replace the conditional transformer-based denoising structure. (1) After replacing with "concat+MLP", model performance presents a significant decline, verifying that simple concatenation operation may not learn two conditional signals effectively. (2) Compared with "cross-attention", our proposal achieves better performance, revealing that multi-stacks of transformer structure are capable of capturing the content condition and user sequence simultaneously.Propagation Path Enhanced Detection. "w/o directed graph", "w/o hypergraph" are implemented without directed propagation graph and user hypergraph. The "sequence encoder" is denoted as to replace with user sequence encoder. (1) Both modules are crucial to achieving desirable detection performance as "w/o hypergraph" and "w/o directed graph" only achieve degraded results. It demonstrates both global structure feature and sequence depth feature are beneficial for detection performances. (2) Without the both two graph modules, performance declines significantly due to sequence information can not reflect the propagation temporal and structure information effectively.Here we conduct hyper-parameter sensitivity analysis on two parameters: the number of diffusion step 𝑇 and the guidance strength 𝑤. In Figure 5 (a), with the increase of 𝑇 , model performance first increases and then significantly drops. A greater number of diffusion steps are required to obtain more refinement, while the generated embeddings do not fit the distribution of real propagation paths if 𝑇 is too small. Figure 5 (b) shows that with the increase of conditional guidance strength 𝑤, model performance first improves and then drops. This demonstrates that too much conditional signal may hurt the quality of the user embedding.In this section, different generative models are compared in Table 4. We take the deep generative model C-VAE [23] and C-GAN [17] as baselines. The proposed DGA-Fake outperforms the generative models over three datasets. Due to the diffusion-guided model generating diverse and high-quality propagation paths, our proposal is capable of capturing the temporal and structural features to facilitate the detection.Figure 6 presents the case of generated propagation path. One can clearly see that the generated paths are similar to the ground-truth in terms of high-frequency user, initial spreading user, propagation sequence and sub-graph structure. It demonstrates that our proposal learns the real propagation feature in the user community, capturing the temporal and structural features during news spreading. 
related work: 
 We divide detection methods into two categories. Content-based: many methods focus on content features for detection [13, 21, 22, 33, 39] . Some recently works deploy large language model by prompt engineering [2, 36] . Propagation-enhanced: some studies apply propagation path to detect fake news [8, 27, 31, 44] . Besides, some researchers integrate auxiliary information for early detection [19, 24, 32, 42] . More details about related work are in Appendix A.5. 
conclusion: 
 In this paper, we propose a novel Diffusion Guided Propagation Augmentation Fake News Detection model. We generate the propagation paths via conditional diffusion module and further incorporate them with news content feature for fake new detection. Experimental results evaluated on three popular datasets demonstrate the superiority of our proposal. 
 
 Appendix:  


Decision: This paper focuses on multi-modal fake news detection leveraging not only the content but also the propagation network structures. To address the propagation structure incompleteness at the early stage of fake news spread, the authors propose to generate the propagation paths based on the guidance of the diffusion process. All reviewers acknowledge the contribution regarding the idea, the method design, and good experimental results. The overall writing is of good quality. However, they also expressed their concerns regarding the result significance, the sufficiency of related work citing and discussing, the sufficiency of experiments, and the paper structure (only 7 pages for main content considering an 8-page main content limitation).

After rebuttal, no reviewers chose to change their recommendation. The two reviewers providing negative ratings still have concerns about the recency of the added baselines and the revision workload to make it to be a full 8-page paper when preparing the next version. Overall, I believe this submission provides a novel idea and has a good execution of method design and experiments after rebuttal. By checking the updated version in the anonymous GitHub repository, the workload risk of revision is finally lowered. I did not find very strong and critical aspects to reject this paper if every revisions and additions are well presented in the final version.

Reviews: review: paper_summary: paper_summary: This paper proposes a novel approach called DGA-Fake for early detection of fake news on social media platforms using diffusion-guided propagation path generation. Unlike previous methods that rely solely on content analysis or require extensive propagation data, DGA-Fake generates realistic propagation paths based on news content and historical user interactions in the early stages of news dissemination. The model employs a dual conditional denoiser module to guide the generation process and incorporates the generated paths into a propagation-enhanced detection module to capture global structure and temporal features. Experiments on three real-world datasets demonstrate the superiority of DGA-Fake compared to state-of-the-art baselines.
strength_weakness: strength_weakness: Pros:
1. Addresses the dilemma between detection efficacy and social hazard by generating propagation paths in the early stages, reducing the potential harm caused by the spread of fake news.
2. Utilizes a novel diffusion-guided approach to generate realistic propagation paths based on news content and historical user interactions, providing rich social context for detection.
3. Incorporates a dual conditional denoiser module to effectively control the generation process and integrate multi-condition features.
4. Employs a propagation path enhanced detection module to capture global structure and temporal depth information from the generated paths, improving detection performance.
5. Consistently outperforms state-of-the-art baselines on three real-world datasets, demonstrating its effectiveness in early fake news detection.
limitations: limitations: Cons:
1. The main concern is that the related works are highly insufficient. Highly related references [1,2,3,4] are missing. It is strongly suggested the authors may comprehensively discuss the related works in the related work section, even in the appendix. Otherwise, this paper may mislead the readers.
2. It is not clear whether or not the authors conduct multiple runs for each results. Standard deviation is desired if the author conduct multiple runs. Otherwise, the results are not convincing.
3. Statistical testing is desired. It is highly recommended that the authors conduct a Statistical testing. Otherwise, the statistical significance of the results are not clear.
4. It is unclear whether the generated Propagation Path actually matches the real-world Propagation Path. More explanations in this aspect are desired. It is also suggested that the authors may verify the accuracy of generated Propagation Path. Otherwise, the motivation is not convincing enough.

[1] A Multitask, Multi-lingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity. https://arxiv.org/abs/2302.04023

[2] Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4 EMNLP 2023 https://arxiv.org/abs/2305.14928

[3] Fact-Checking Complex Claims with Program-Guided Reasoning ACL 2023 https://aclanthology.org/2023.acl-long.386/

[4] Combating Misinformation in the Age of LLMs: Opportunities and Challenges https://arxiv.org/abs/2311.05656
paper_summary: paper_summary: The authors investigated the early detection of fake news by simulating realistic propagation paths based on news content before
actual spreading. Specifically, they proposed a generative model, DGA-Fake, which generates the propagation paths via a conditional diffusion module and further incorporates them with news content features for fake news detection. Evaluation across three datasets demonstrates the effectiveness of their proposed model.
strength_weakness: strength_weakness: This paper is well-motivated and provides new ideas for early rumor detection by predicting propagation paths. 
This paper is well organized, with algorithms and formulas clearly detailing the flow and structure of the proposed model.
limitations: limitations: The methodology section involves numerous hyperparameters, but the parameter analysis part is somewhat insufficient. And since you're utilizing hypergraphs in your method, it might be more comprehensive to include a hypergraph-based method to Propagation-enhanced methods as baselines.
There are some typos. In Figure 4, it should be e3 instead of e2, And “w/o all conditions” stands for without news content, user sequence, and both.” In 4.3 seems repetitive.
paper_summary: paper_summary: This paper proposes enhancing early fake news detection by using propagation path generation for the first time. The authors introduce the DGA-Fake model, which creatively generates reasonable propagation paths through controllable guided diffusion. The model integrates global propagation structure and temporal depth information via those propagation paths to improve fake news detection.
strength_weakness: strength_weakness: 1. **Well-Written Manuscript:** The authors clearly explain why using propagation path generation for fake news detection is beneficial. They outline the challenges of this approach and their solutions, making it easy for readers to understand the paper's motivation and main contributions.
2. **Novelty:** By leveraging the innovative approach of using diffusion models to generate propagation paths, the authors offer a new perspective on enhancing the detection process.
3. **Extensive Experiments:** The authors conducted extensive experiments to validate their model’s performance. Overall, it is a solid piece of work.
4. **Code Availability:** The authors provide their code, ensuring that other researchers can replicate and validate the experiments. This transparency is crucial for advancing academic research.
limitations: limitations: 1. **Method Details:** The process for obtaining user embeddings is not clarified. Additionally, some variables in the paper lack direct interpretation. 
2. **Typos:** There are typographical errors in the paper, such as the abbreviation of "Gossip" in Figure 5 ("Goosi."), "sigmod" in line 548, and the inconsistency of "PHEME" and "Pheme" in Tables 1, 2, and 3. Correcting these errors will improve the manuscript's clarity.
3. **Ablation Study:** The authors could provide results for detection performance using only content and only propagation. This would offer a clearer understanding of the enhancement brought by propagation path augmentation.
4. **Dataset Details:** A more detailed description of the datasets would be beneficial. Information such as the average number of propagation sequences per news post and the average length of these sequences would help readers better understand the data condition.
5.  **Paper Structure**: The authors have compressed the main content to 7 pages ( the page limit is 8 in fact), placing many details in the appendix. Some of this supplementary content could be integrated into the main text to enhance the paper’s readability and coherence.
paper_summary: paper_summary: The paper investigates the problem of early fake news detection through propagation path generation, and the authors propose a novel generative model, DGA-Fake, which integrates a Gaussian-based bootstrap diffusion module that helps to progressively denoise and generate plausible user embeddings of the next interactions from randomly sampled Gaussian noise, which can be used to generate a simulated sequence of user interactions guided by the history of the interactions and news content. The authors use the propagation path enhancement detection module to learn temporal depth information through user sequence hypergraphs.DGA achieves early detection of fake news by integrating the propagation of global structure and temporal sequence information.
strength_weakness: strength_weakness: 1. The paper introduces a novel approach, DGA-Fake, for early fake news detection by generating propagation paths based on content features and historical interactions. The incorporation of a guided diffusion module to simulate user interactions at the early stage of news dissemination showcases a unique theoretical approach to enhancing detection capabilities while mitigating social hazards.
2.  Strong experiment performance. The technical correctness is supported by the thorough evaluation conducted across three datasets, demonstrating the superiority of the proposed approach. 
3. Well-structured paper. The clarity of the paper in explaining the methodology, experimental setup, and results enhances its readability and understanding.
limitations: limitations: 1. Insufficient Comparison with State-of-the-Art Methods: The authors did not compare articles on fake news detection after 2023.

2. Lack of clarity on the advantages of diffusion modeling: the authors do not clearly state why they chose to use diffusion modeling for generating user embeddings and paths, and the advantages of diffusion modeling over other approaches.

3. The MM official instructions state that the body section of the article can be 8 pages long, but the author only wrote 7 pages of content, so why not put the content from the supplemental material in the body of the paper?