On the Involvement of Bots in Promote-Hit-and-Run Scams – The Case of Rug Pulls

[EN] Many social media frauds related to finance can be summarized under what we consider promote-hit-and-run scams. Examples include rug pull scams also known as exit scams, pump-and-dump schemes or bogus crypto currency trading platforms. For scams of this kind to work they must be publicly advertised as lucrative investment opportunities disguising the fraudulent motivation behind them. Social media are key in this promotion. Here, fraudsters find platforms to persuade others investing into what later turns out to be a scam. Via social network analysis of Twitter screen names and their first-level contacts, our work investigates rug pulls. It is aimed at profiling social media communication around them with a special focus on the deployment of bots. Repeatedly bots have been identified in social media campaigns (Orabi et al., 2020). Bot deployment in the context of rug pulls, however, has not been studied yet. Our analysis of social data of 27 rug pulls reveals massive bot activity coordinated within and between rug pulls mainly targeting established finance news outlets, e.g., Bloomberg, Reuters. Among the conclusions of our work is that bot deployment may prove an early indicator for rug pulls and other promote-hit-and-run scams.Federal Ministry of Education and Research of Germany (BMBF)Janetzko, D.; Krauß, J.; Haase, F.; Rath, O. (2023). On the Involvement of Bots in Promote-Hit-and-Run Scams – The Case of Rug Pulls. Editorial Universitat Politècnica de València. 187-194. https://doi.org/10.4995/CARMA2023.2023.1642818719

Effect of Twitter investor engagement on cryptocurrencies during the COVID-19 pandemic

This study aims to examine whether the prices and returns of two cryptocurrencies, Dogecoin and Ethereum, are affected by Twitter engagement following the COVID-19 pandemic. We use the autoregressive integrated moving average with explanatory variables model to integrate the effects of investor attention and engagement on Dogecoin and Ethereum returns using data from December 31, 2020, to May 12, 2021. The results provide evidence supporting the hypothesis of a strong effect of Twitter investor engagement on Dogecoin returns; however, no potential impact is identified for Ethereum. These findings add to the growing evidence regarding the effect of social media on the cryptocurrency market and have useful implications for investors and corporate investment managers concerning investment decisions and trading strategies

FINFLUENCERS: OPINION MAKERS OR OPINION FOLLOWERS?

This paper explores the concept of Finfluencers: financial social network actors with high potential social influence. Our research aims to clarify whether Finfluencers drive or are influenced by the broader social network sentiment, thereby establishing their role as either opinion makers or opinion followers. Using a dataset of 71 million tweets focusing on stocks and cryptocurrencies, we grouped actors by their social networking potential (SNP). Next, we derived sentiment time series using state-ofthe- art sentiment models and applied the technique of Granger causality. Our findings suggest that the sentiment of Finfluencer actors on Twitter has short-term predictive power for the sentiment of the larger group of actors. We found stronger support for cryptocurrencies in comparison to stocks. From the perspective of financial market regulation, this study emphasizes the relevance of understanding sentiment on social networks and high social influence actors to anticipate scams and fraud

Manipulating the Online Marketplace of Ideas

Social media, the modern marketplace of ideas, is vulnerable to manipulation. Deceptive inauthentic actors impersonate humans to amplify misinformation and influence public opinions. Little is known about the large-scale consequences of such operations, due to the ethical challenges posed by online experiments that manipulate human behavior. Here we introduce a model of information spreading where agents prefer quality information but have limited attention. We evaluate the impact of manipulation strategies aimed at degrading the overall quality of the information ecosystem. The model reproduces empirical patterns about amplification of low-quality information. We find that infiltrating a critical fraction of the network is more damaging than generating attention-grabbing content or targeting influentials. We discuss countermeasures suggested by these insights to increase the resilience of social media users to manipulation, and legal issues arising from regulations aimed at protecting human speech from suppression by inauthentic actors.Comment: 25 pages, 8 figures, 80 reference

Exploring Alternative Approaches for TwitterForensics: Utilizing Social Network Analysis to Identify Key Actors and Potential Suspects

SNA (Social Network Analysis) is a modeling method for users which is symbolized by points (nodes) and interactions between users are represented by lines (edges). This method is needed to see patterns of social interaction in the network starting with finding out who the key actors are. The novelty of this study lies in the expansion of the analysis of other suspects, not only key actors identified during this time. This method performs a narrowed network mapping by examining only nodes connected to key actors. Secondary key actors no longer use centrality but use weight indicators at the edges. A case study using the hashtag "Manchester United" on the social media platform Twitter was conducted in the study. The results of the Social Network Analysis (SNA) revealed that @david_ornstein accounts are key actors with centrality of 2298 degrees. Another approach found @hadrien_grenier, @footballforall, @theutdjournal accounts had a particularly high intensity of interaction with key actors. The intensity of communication between secondary actors and key actors is close to or above the weighted value of 50. The results of this analysis can be used to suspect other potential suspects who have strong ties to key actors by looking.SNA (Social Network Analysis) is a modeling method for users which is symbolized by points (nodes) and interactions between users are represented by lines (edges). This method is needed to see patterns of social interaction in the network starting with finding out who the key actors are. The novelty of this study lies in the expansion of the analysis of other suspects, not only key actors identified during this time. This method performs a narrowed network mapping by examining only nodes connected to key actors. Secondary key actors no longer use centrality but use weight indicators at the edges. A case study using the hashtag "Manchester United" on the social media platform Twitter was conducted in the study. The results of the Social Network Analysis (SNA) revealed that @david_ornstein accounts are key actors with centrality of 2298 degrees. Another approach found @hadrien_grenier, @footballforall, @theutdjournal accounts had a particularly high intensity of interaction with key actors. The intensity of communication between secondary actors and key actors is close to or above the weighted value of 50. The results of this analysis can be used to suspect other potential suspects who have strong ties to key actors by looking

Studi Netnografi Pola Komunikasi Jaringan Komunitas Cryptocurrency Dogecoin Pada Twitter

Cryptocurrency Dogecoin awalnya dianggap sebagai meme coin namun telah mengalami kenaikan nilai tukar sebanyak 800% pada Januari 2021 dan bertambah lagi sebesar 400% pada April 2021. Hal ini tidak lepas dari dukungan kuat dari komunitas cryptocurrency Dogecoin dan top public profiles pada media sosial Twitter. Penelitian ini menggunakan metode digital netnography untuk melihat pola komunikasi jaringan komunitas cryptocurrency Dogecoin di Twitter. Komunitas yang diteliti tidak terpusat pada akun komunitas tertentu namun meliputi seluruh akun Twitter yang aktif berdiskusi mengenai Dogecoin. Batasan penelitan adalah pada tanggal 1 April - 9 Mei 2021 bertepatan dengan beberapa peristiwa penting yang terjadi. Data yang digunakan adalah semua percakapan pada Twitter dengan kata kunci "Doge" dan diambil menggunakan social network analysis tools Brand24 dan Netlytic. Penelitian ini menemukan adanya 5 tipe interaksi yang merupakan pola komunikasi jaringan Dogecoin. Pola komunikasi yang ditemukan pada penelitian ini dapat memberikan masukan bagi pengembang Dogecoin dan cryptocurrency lainnya tentang pentingnya memberikan informasi yang dapat meyakinkan komunitas untuk tetap hold sebuah cryptocurrency. Kemudian pentingnya membina komunitas yang saling mendukung dan memberi semangat di antara anggota komunitas, dan pentingnya bekerjasama dengan top public profiles untuk memberikan keyakinan dan konfirmasi untuk mengatasi keresahan komunitas terkait volatility yang tinggi dari sebuah cryptocurrency

Detection of Stock Manipulation Influencer Content using Supervised Learning

In recent years, social media influencers have emerged as key players in stock manipulation schemes. Despite their growing impact, methods to detect such activities remain scarcely explored. In this study, we examine the social media content of stock manipulation influencers (SMIs) implicated in a $100 million fraud case by the U.S. Securities and Exchange Commission (SEC) in 2022. Leveraging natural language processing (NLP) techniques, we first investigate the linguistic characteristics present in the social media content published by SMIs. Next, we develop and evaluate supervised learning models to detect manipulative content. Our results have significant implications for investors, regulators, and the broader financial community. They reveal the unique linguistic characteristics of SMI content and demonstrate the potential of machine-learning and deep-learning-based techniques in advancing fraud detection systems