BIC : T witter Bot Detection with Text-Graph Interaction and Semantic Consistency (2023)

Abstract: Twitter bots are automatic programs operated by malicious actors to manipulate public opinion and spread misinformation. Research efforts have been made to automatically identify bots based on texts and networks on social media. Existing methods only leverage texts or networks alone, and while few works explored the shallow combination of the two modalities, we hypothesize that the interaction and information exchange between texts and graphs could be crucial for holistically evaluating bot activities on social media. In addition, according to a recent survey (Cresci, 2020), Twitter bots are constantly evolving while advanced bots steal genuine users’ tweets and dilute their malicious content to evade detection. This results in greater inconsistency across the timeline of novel Twitter bots, which warrants more attention. In light of these challenges, we propose BIC, a Twitter Bot detection framework with text-graph Interaction and semantic Consistency. Specifically, in addition to separately modeling the two modalities on social media, BIC employs a text-graph interaction module to enable information exchange across modalities in the learning process. In addition, given the stealing behavior of novel Twitter bots, BIC proposes to model semantic consistency in tweets based on attention weights while using it to augment the decision process. Extensive experiments demonstrate that BIC consistently outperforms state-of-the-art baselines on two widely adopted datasets. Further analyses reveal that text-graph interactions and modeling semantic consistency are essential improvements and help combat bot evolution.

## introduction twitter bots are controlled by automated programs and manipulated to pursue malicious goals such as advocating for extremism and producing spam @xcite . bots are also involved in spreading misinformation during the pandemic @xcite . since twitter bots pose a threat to online society, many efforts have been devoted to detecting bots. the majority of the existing approaches are textbased and graph-based. the text-based methods analyze the content to detect twitter bots by natural language processing techniques. @xcite adopted recurrent neural networks to extract textual information. @xcite utilized the pre-trained language model bert to help detect bots. the graph-based methods model the twittersphere as graphs and adopt geometric neural networks or concepts of network dynamics to identify bots. @xcite al. (2022a) however, existing methods are faced with two challenges. on the one hand, these methods only adopt texts or graphs alone, and only a few works shallowly combine the two modalities as figure 1 (a) shows. the text-based model can not get the graph modality information while the graphbased model can not get the text modality information. we hypothesize that it is wise to interact and exchange information between texts and graphs to evaluate bot activities. on the other hand, @xcite pointed out that twitter bots are constantly evolving. advanced bots steal genuine users' tweets and dilute their malicious content to evade detection, which results in greater inconsistency across the timeline of advanced bots as figure 1 (b) illustrates. previous methods can not capture this characteristic. namely, there is an urgent need for a method that can identify advanced bots. in light of these challenges, we propose a framework bic (twitter bot detection with text-graph interaction and semantic consistency). bic separately models the two modalities, text and graph, in social media. a text module is adopted to encode the textual information and a graph module is used to encode graph information. bic employs a text-graph interaction module to enable information exchange among different modalities in the learning process. to capture the inconsistency of advanced bots, bic leverages a semantic consistency module, which employs the attention weights and a sample pooling function. our main contributions are summarized as follows: ## problem definition we first define the task of twitter bot detection with the text and graph modality. for a twitter user u @xmath0, the text modality contains the description b i and the tweets s @xmath1,j } t i @xmath2, where t i denotes the tweet count. the graph modality contains the attribution f i of u i and the heterogeneous graph @xmath3, e, φ, r e ), where u denotes the user set, e denotes the edge set, φ : e -→ r e denotes the relation mapping function and r e is the relation type set. the neighbors of u i can be derived from g as ## methodology figure 2 shows an overview of our proposed framework named bic. specifically, bic first leverages a text module to encode textual information and a graph module to encode graph information. bic then adopts a text-graph interaction module to interact and exchange modality information in the learning process. to further interact the two modalities, bic repeats this process for m times. bic extracts the semantic consistency from the attention weights derived from the text module with the help of the semantic consistency module. finally, bic leverages text modality, graph modality, and semantic consistency vectors to identify bots. ## conclusion twitter bot detection is a challenging task with increasing importance. to conduct a more comprehensive bot detection, we proposed a bot-detection model named bic. bic interacts and exchanges information across text modality and graph modality by a text-graph interaction module. bic contains a semantic consistency module that derives the inconsistency from tweets by the attention weight to identify advanced bots. we conducted extensive experiments on two widely used benchmarks to demonstrate the effectiveness of bic in comparison to competitive baselines. further experiments also bear out the effectiveness of modality interaction and semantic consistency detection. in the future, we plan to explore better interaction approaches.