Temporal Graph Analysis of Misinformation Spreaders in Social Media (2022)

Abstract: Proactively identifying misinformation spreaders is an important step towards mitigating the impact of fake news on our society. Although the news domain is subject to rapid changes over time, the temporal dynamics of the spreaders’ language and network have not been explored yet. In this paper, we analyze the users’ time-evolving semantic similarities and social interactions and show that such patterns can, on their own, indicate misinformation spreading. Building on these observations, we propose a dynamic graph-based framework that leverages the dynamic nature of the users’ network for detecting fake news spreaders. We validate our design choice through qualitative analysis and demonstrate the contributions of our model’s components through a series of exploratory and ablative experiments on two datasets.

## introduction with the popularity of social media platforms constantly increasing, the dissemination of false online information becomes a major hurdle, having catastrophic effects on our society @xcite . it is essential to address this issue early on; to efficiently and rapidly identify misinformation spreaders and spurious accounts which are likely to propagate posts from unreliable news sources. to this end, we introduce an early warning model that distinguishes authors who have repeatedly shared news from unreliable sources in the past, from those that share news from reliable sources. we use the terms 'misinformation spreaders' and 'real news spreaders' for each user class, respectively. in this paper, the term misinformation is used as an umbrella term that covers misinformation, disinformation, partisan news and satirical content. figure 1 depicts examples of the posting activity for each user class. recently, significant attention has garnered towards graph representational learning methods @xcite due to their advances in various nlp domains. @xcite use a graph-based approach to model the semantic relationship between sentences in a document for fake news detection. @xcite apply graph neural networks to explore the social network of misinformation spreaders and show that interpersonal trust plays a significant role in differentiating them from real news spreaders. such graph approaches are able to model user-to-user relationships and therefore provide a promising underexplored research direction for identifying misinformation spreaders. the impact of time on fake news prediction has made the task even more challenging, as the content-based differences of news sources change due to the highly dynamic nature of the news topics @xcite . most of the fake news detection methods that use static features need to be continuously updated with new annotated data to stay relevant @xcite . we argue that this hypothesis can be generalized for detecting misinformation spreaders. similarly to feature-based methods, existing graph modeling approaches are not specifically designed for learning the time-evolving similarities of the users' interactions. addressing these limitations of existing research, we propose an approach accounting for the temporal dynamics of user-to-user relationships instead. we introduce a model that extracts features from users' content similarities and social interactions and models the temporal evolution of these connections in order to identify misinformation spreaders. in addition, our study aims to answer the following research questions: rq1: do the users' semantic similarities and social interactions fluctuate over time? rq2: are temporal relationships indicative of misinformation spreading behavior? for the first exploration, we formulate the problem as a binary classification task, with a potential for a more fine-grained approach in the future. we first build dynamic linguistic and social graphs, which are constructed based on the users' posting behaviour within consecutive time-windows. subsequently, the generated temporal graph representations are treated as a sequence of features for the final classification. to the best of our knowledge, dynamic graph modelling has not been utilized for identifying misinformation spreaders in other works. we conduct a series of exploratory analyses in the user-to-user relationships. through ablative experiments, we show the effectiveness of our model's components for profiling misinformation spreaders. our contributions are as follows: ## background and related work while user profiling approaches have been investigated for various tasks, it wasn't until after the pan 2020 competition @xcite that the problem of misinformation spreaders identification gained the attention of the research community. most recent studies are focused on analyzing emotional signals @xcite , personality and linguistic patterns @xcite . these methods rely on the assumption that the content, and therefore the features that are extracted, remains constant over time. while static linguistic patterns have proven to be useful features for misinformation spreader detection, none of the current methods explore temporal aspects of their behavior. our model utilizes the users' contextualized content embeddings as user (node) representations and simultaneously leverages their content similarities over time and social interactions dynamically (via edges in the temporal graph). in the context of user modelling, graph representational learning approaches (kipf and welling, 2016; @xcite have made significant advances in enhancing nlp models for various tasks @xcite @xcite @xcite . @xcite identified misinformation spreaders by extracting features from a network that is built based on interpersonal trust metrics. despite their success, a limitation of the existing approaches is that they do not account for the temporal dynamics of the semantic and social connections. we argue that the users' characteristics and interactions change dynamically over time due to the dynamic nature of the news cycle, therefore temporal graphs are more suitable to model the evolution of the user-to-user relationships @xcite . our hypothesis, inspired by @xcite , is that both the social and the content similarity patterns of misinformation spreaders differ from those of other users. the concept of temporal graphs has been around for some years @xcite @xcite with numerous applications @xcite @xcite . the most relevant to our work is the model proposed by @xcite , leveraging signals from financial data, social media, and inter-stock relationships via a graph neural network in a hierarchical temporal fashion. we draw inspiration from these approaches and propose a dynamic temporal graph for misinformation spreader detection. twitter dataset. to generalize our content similarity dynamics findings, we utilize in addition the twitter dataset released by @xcite . since the dataset contained the labels and the user ids, we re-crawled the users' posting history. after filtering the users whose handles were deleted or had insufficient data, the resulting dataset contained 3.5k users and 2.6m posts with roughly 40:60 class distribution of fake and real news spreaders respectively. since there are practically no social interactions between the users in this dataset, we report results only with the semantic similarity graphs. we split the dataset into train (70%), development (20%) and test (10%) as in the original paper. ## experimental setup to answer the rq2, we need to investigate the reliability of the temporal semantic and social connections as features for identifying misinformation spreaders in various scenarios. predicting future user behavior. we analyze whether the past user behavior, represented through temporal graphs, can be used to predict their future user behavior. to this end, we use the whole set of users in the training, validation and test, but each set contains data from different time periods. specifically, the training set consists of 8 months (jan-aug 20'), and the validation (sep-dec 20') and test sets (jan-apr 21') 4 months each, resulting in a consecutive 50:25:25 time split of the user's posting history. this stands for both datasets since they were collected around the same time period. we provide a visual depiction of this split in appendix a.5 in figure 8a . generalizing to unseen users. we examine which types of relationships have the ability to generalize to unseen users. in this setup we utilize a user split, where we divide the users into a train:validation:test sets of ratio 70:10:20 using all of their posting history. this split is also visually depicted in appendix a.5 in figure 8b . performance on unseen users in the future. we also aim to test whether the temporal graph features generalize on both unseen users and future content, to this end we utilize the mixed split. we split the users into a train:validation:test sets of ratio 70:10:20, where the train set contains users who have posted the first half (jan-aug 20') of the whole time period, while the validation and test sets contain a different set of users who post on the second half (sep 20'-apr 21'). with this setup, we evidently demonstrate the reliability of the proposed model of detecting misinformation spreaders on unseen data. a visual depiction of this split is provided in appendix a.5 in figure 8c . ## conclusion in this study we proposed a dynamic graph neural network framework that generates temporal graph representations from the users' semantic similarities and social interactions through time. our extensive experiments and ablation study demonstrated that the temporal graphs are more efficient than content-based models or simple static graphs for predicting (a) the future misinformation spreading behavior, (b) the behavior of unseen users, and (c) misinformation spreading behavior in a zero-shot scenario. these results indicate that a model utilizing temporal user relationships is more robust and more efficient for misinformation spreader detection compared to topic-sensitive or time-agnostic models, e.g. talking about trump doesn't make one a misinformation spreader and it is quite normal near election time. through exploratory experiments, we analyzed the various aspects of the framework in order to provide an insight into its usability. these experiments showed that dynamic semantic similarities lead to better results than the social ones. the ablation study on the components of the model revealed that the temporal modelling of the users' semantic similarities and social interactions significantly contributes to identifying misinformation spreaders effectively. our error analysis indicated that the misclassified fake news spreaders tend to post a very low number of fake news posts and a high number of real news posts from highly credible sources. yet, the proposed framework is applicable as a human moderator-assistance tool for identifying users that post fake news more consistently.