S cam S pot: Fighting Financial Fraud in I nstagram Comments (2024)

Abstract: The long-standing problem of spam and fraudulent messages in the comment sections of Instagram pages in the financial sector claims new victims every day. Instagram’s current spam filter proves inadequate, and existing research approaches are primarily confined to theoretical concepts. Practical implementations with evaluated results are missing. To solve this problem, we propose ScamSpot, a comprehensive system that includes a browser extension, a fine-tuned BERT model and a REST API. This approach ensures public accessibility of our results for Instagram users using the Chrome browser. Furthermore, we conduct a data annotation study, shedding light on the reasons and causes of the problem and evaluate the system through user feedback and comparison with existing models. ScamSpot is an open-source project and is publicly available at https://scamspot.github.io/.

## introduction financial fraud has switched its medium -from phone calls and emails to social media @xcite . a recent report from the u.s. federal trade commission shows that the number of social media scams has soared in recent years and especially cryptocurrency scams are initiated on instagram 1 . last year, the topic also gained political attendance 2 , but so far, instagram users have not seen any improvements ( instagram's existing spam filter has a precision of 98.36%, but only a recall of 11.51% ( to close this gap, we show a way to efficiently classify comments with high precision and communicate the results to the user in real-time to improve the user experience and reduce the likelihood of fraud incidents. the solution is scamspot, a system designed to remove spam and scam comments from instagram. more specifically, we contribute as follows: the result is an evaluated and deployed application that enables every instagram user to use the website with a significantly reduced risk of encountering spam and fraud, making the user experience more enjoyable and secure. ## dataset & data annotation study the development of scamspot necessitates the creation of a specialized dataset, as existing research lacks robust data samples for instagram comments, particularly in the financial sector. recognizing the gap, we embark on a two-fold mission: not only to gather this essential data but also to annotate it meticulously, thereby contributing a valuable resource to the community. to address this need, we develop a python script utilizing an existing library 3 to access instagram's private api. between february 28th and may 4 th 2023, we collected data from 38 instagram pages related to finance and cryptocurrencies. this effort yields a dataset of over 100,000 comments, which, to our knowledge, represents one of the largest publicly available datasets in this niche. we have made this dataset, along with the scraping script, openly accessible, enabling others to benefit from and expand upon our work. comment examples can be found in the pivotal aspect of our study was the annotation of 3,445 comments (66.6% genuine, 33.4% spam/scam). we annotate the dataset ourselves using a simple, self-developed web 3 https://github.com/adw0rd/instagrapi interface. while one of the team members has several years of experience as an owner of multiple large financial instagram pages, the quality of the annotated comments was validated by continuously checking a subset of comments against the later hand-picked experts' classifications. to better understand how expertise in the financial sector influences the identification of spam and scams we perform a data annotation study. we divide participants into two groups: 'experts' with substantial industry knowledge and 'amateurs' with less or no such experience. this distinction is critical, as it highlights the challenges faced by non-experts in recognizing fraudulent content, a key factor in the importance of scamspot. while experts reach a fleiss kappa agreement of 0.618, amateurs manage only 0.519 @xcite . this disparity underscores the necessity of expert knowledge in accurately classifying such comments. to solidify these results, we conduct a follow-up study with 11 handpicked industry experts, resulting in a near-perfect fleiss kappa score of 0.808. these results not only validate our approach but also emphasize the nuanced differences in definitions of spam and scam among professionals. the insights from this annotation study are instrumental in shaping scamspot. they not only inform the training of our models but also highlight the real-world challenge faced by everyday instagram users, particularly the less experienced ones, in navigating financial fraud. this aspect is later also mirrored in the performance of advanced language models like gpt-3 and gpt-4, which also struggle with categorizing these comments, further accentuating the complexity of the task and the value of our expert-driven approach. to sum up, our efforts in data collection and annotation are not just preliminary steps but foundational to the development of scamspot. by making these resources publicly available, we aim to facilitate further research in this vital area, emphasizing the importance of domain-specific expertise in combating financial fraud on social media platforms. ## model considerations the next objective is to demonstrate an effective system rather than conduct an exhaustive analysis of various models. nevertheless, we test a variety of models to identify an effective solution for detecting fraudulent instagram comments. the selection of models includes traditional statistical models, large language models and bert as a representative of the transformer models. the following specific models are selected: we assess the advanced natural language processing capabilities of gpt-3 ("gpt-3.5-turbo") and gpt-4 ("gpt-4-1106preview"). their ability to understand and generate human-like text is a key consideration in our analysis. however, the lack of transparency and control over the training data of these models is a major limitation. despite the adjustment of seed and temperature parameters ( in this study, we adopt a zero-shot approach with llms like gpt-3 and gpt-4, contrasting them with the fine-tuned bert model. this methodology is chosen to demonstrate the practical usability of general-purpose llms in their standard configuration. while models like gpt-3 and gpt-4 show proficiency in general tasks, our findings align with those of @xcite , when illustrating their limitations in specific tasks compared to fine-tuned models. this highlights the necessity of model selection tailored to task specificity and resource availability. despite the hype around large language models, the results in this leads us to the decision to select a finetuned bert model for scamspot. the reasons also include: ## scamspot to deliver comment classifications to users in real-time efficiently, we develop a chrome browser extension, realizing the scamspot architecture. this approach is inspired by @xcite , which demonstrates the effectiveness of presenting classification results through a chrome extension. our approach differs by directly displaying the classifications without requiring users to do anything other than use the instagram website. this approach allows the user to take full advantage of the ai model without compromising the user experience. our system design consists of 3 main components. the fine-tuned bert model enables the classification of instagram comments. the chrome browser extension, which is installed by the user and runs on the user's device, allows us to extract the comments, communicate with our api, and manipulate the html dom to display the results in real-time. a rest api acts as the connection between the browser extension and the bert model. ## system evaluation scamspot's effectiveness is evaluated in two cycles based on hevner's design science research framework @xcite , which emphasizes the iterative development and refinement of a system through multiple cycles of design, testing, and feedback. in each cycle, both qualitative and quantitative evaluation metrics are considered. the quantitative results focus on the metrics and the effectiveness of the model itself. as can be seen in our model exhibits notable performance compared to other baseline models as well as openai's gpt-3 and gpt-4, justifying its utilization in the project. the f1 scores can be found in when comparing the results with instagram's existing spam filter, the model also performs well. while instagram's spam filter only achieves a recall of 11.51%, our model achieves 92.13%. however, we must give credit to instagram's spam filter as its precision was 98.36%, while our model only achieves 92.86%. this shows room for improvement. another notable result is that both gpt models fail to correctly categorise comments as spam/scam or genuine. despite multiple approaches and different prompts, the results are surprisingly disappointing. results can be found in not only the quantitative but also the qualitative results improve drastically after the second cycle, in which the feedback from the first evaluation cycle was implemented. 20 scamspot users are asked in a questionnaire about their experiences with the system and their feedback. when using the prototype, almost all test users report that the user experience has improved and that they are more willing to interact with others in the comments section. with the browser extension, users report overall positive feelings of joy and excitement compared to frustration and annoyance without scamspot, as shown in figure 3 . ## related work in recent years, academic steps have already been taken regarding spam detection in social networks @xcite . most research has focused on twitter, partly because it is easier to scrape data for training purposes and therefore ignored instagram. still, progress has also been made on instagram, but more around account-based spam detection @xcite @xcite . the few comment-based approaches have so far focused on general spam, not financial fraud in instagram comments. a qualitative comparison of similar projects and papers can be found in the idea of using a browser extension and a rest api originated from a research team who based their work on the results of study [1] from their system differentiates itself from ours since it focuses on comments under posts from indonesian celebrities and users of the prototype still had to manually click on comments to check if they were spam. our goal was to further improve the concept and ensure people see results directly within the website without having to click anything. furthermore, a user evaluation also differentiates our work to ensure validated results. the decision to fine-tune a bert model @xcite was heavily influenced by the papers published shortly before our project which all archived great results in spam detection @xcite @xcite . however, to our knowledge, there have been no studies looking at spam and fraud in comments under financial-related instagram content. ## conclusion and further work we introduce scamspot as an application that enables instagram users to navigate the social media platform more safely by detecting fraudulent comments and removing unpleasant spam messages from the comment section of instagram posts. we have shown that the combination of the fine-tuned bert model and the chrome browser extension results in a measurably better user experience and can reduce the number of fraud cases during active use. our solution is scalable and allows users with no technical background to safely use the application. this work also contributes a scraped dataset of over 100,000 comments and an annotated dataset of 3,345 comments, which will help future projects and provide the results of our data annotation study. in addition, we evaluate our scamspot on both a quantitative and qualitative level and achieve excellent performance and a positive user impact. to sum up, we show for the first time the viability and effectiveness of a fine-trained bert for this classification problem, setting a precedent for future research on this issue. we further highlight the importance of combating spam and scams on instagram, underscoring the need for solutions like scamspot. in future developments, it is crucial to consider implementing more sophisticated machine learning models to enhance classification accuracy. this includes exploring the capabilities of newer variants of bert and other encoder-only models such as deberta, which have demonstrated superior performance in various classification