Talking Transactions: Decentralized Communication through Ethereum Input Data Messages (IDMs)

• URL: http://arxiv.org/abs/2505.24724v1
• Date: Fri, 30 May 2025 15:47:13 GMT
• Title: Talking Transactions: Decentralized Communication through Ethereum Input Data Messages (IDMs)
• Authors: Xihan Xiong, Zhipeng Wang, Qin Wang, Endong Liu, Pascal Berrang, William Knottenbelt,
• Abstract summary: We focus on the input data field of transactions, which is designed to allow external callers to interact with smart contracts.<n>Users can leverage these Input Data Messages (IDMs) for peer-to-peer communication.<n>We present the first large-scale analysis of IDMs from the genesis block to February 2024 (3134 days)
• Score: 3.838621014068589
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Can you imagine, blockchain transactions can talk! In this paper, we study how they talk and what they talk about. We focus on the input data field of Ethereum transactions, which is designed to allow external callers to interact with smart contracts. In practice, this field also enables users to embed natural language messages into transactions. Users can leverage these Input Data Messages (IDMs) for peer-to-peer communication. This means that, beyond Ethereum's well-known role as a financial infrastructure, it also serves as a decentralized communication medium. We present the first large-scale analysis of Ethereum IDMs from the genesis block to February 2024 (3134 days). We filter IDMs to extract 867,140 transactions with informative IDMs and use LLMs for language detection. We find that English (95.4%) and Chinese (4.4%) dominate the use of natural languages in IDMs. Interestingly, English IDMs center on security and scam warnings (24%) with predominantly negative emotions, while Chinese IDMs emphasize emotional expression and social connection (44%) with a more positive tone. We also observe that longer English IDMs often transfer high ETH values for protocol-level purposes, while longer Chinese IDMs tend to involve symbolic transfer amounts for emotional intent. Moreover, we find that the IDM participants tend to form small, loosely connected communities (59.99%). Our findings highlight culturally and functionally divergent use cases of the IDM channel across user communities. We further examine the security relevance of IDMs in on-chain attacks. Many victims use them to appeal to attackers for fund recovery. IDMs containing negotiations or reward offers are linked to higher reply rates. We also analyze IDMs' regulatory implications. Their misuse for abuse, threats, and sexual solicitation reveals the urgent need for content moderation and regulation in decentralized systems.

Related papers

• MemeChain: A Multimodal Cross-Chain Dataset for Meme Coin Forensics and Risk Analysis [52.468043639056596]
  The meme coin ecosystem has grown into one of the most active yet least observable segments of the cryptocurrency market.<n>MemeChain integrates on-chain data with off-chain artifacts, including website HTML source code, token logos, and linked social media accounts.<n>We quantify the ecosystem's extreme volatility, identifying 1,801 tokens (5.15%) that cease all trading activity within just 24 hours of launch.
  arXiv  Detail & Related papers  (2026-01-28T14:42:02Z)
• Cost-Effective Communication: An Auction-based Method for Language Agent Interaction [15.493640295624994]
  We introduce the Dynamic Auction-based Language Agent (DALA), a novel framework that treats communication bandwidth as a scarce and tradable resource.<n>Our DALA achieves new state-of-the-art performance across seven challenging reasoning benchmarks, including 84.32% on MMLU and a 91.21% pass@1 rate on HumanEval.
  arXiv  Detail & Related papers  (2025-11-17T10:00:20Z)
• Toxic Ink on Immutable Paper: Content Moderation for Ethereum Input Data Messages (IDMs) [5.517460488736803]
  Decentralized communication is becoming an important use case within Web3.<n>As IDMs gain wider adoption, there has been a growing volume of toxic content on-chain.<n>We propose two moderation frameworks for IDMs: (i) BUILDERMOD, where builders perform semantic checks during block construction; and (ii) USERMOD, where users proactively obtain moderation from external classifiers and embed them in transactions.
  arXiv  Detail & Related papers  (2025-10-12T19:02:57Z)
• Sentiment Analysis in Twitter Social Network Centered on Cryptocurrencies Using Machine Learning [1.9336815376402718]
  This paper investigates the opinions of Iranian users on the Twitter social network about cryptocurrencies.<n>It provides the best model for classifying tweets based on sentiment.
  arXiv  Detail & Related papers  (2025-01-16T16:15:52Z)
• Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations [58.65755268815283]
  Many real dialogues are interactive, meaning an agent's utterances will influence their conversational partner, elicit information, or change their opinion. We use this fact to rewrite and augment existing suboptimal data, and train via offline reinforcement learning (RL) an agent that outperforms both prompting and learning from unaltered human demonstrations. Our results in a user study with real humans show that our approach greatly outperforms existing state-of-the-art dialogue agents.
  arXiv  Detail & Related papers  (2024-11-07T21:37:51Z)
• Emotion and Intent Joint Understanding in Multimodal Conversation: A Benchmarking Dataset [74.74686464187474]
  Emotion and Intent Joint Understanding in Multimodal Conversation (MC-EIU) aims to decode the semantic information manifested in a multimodal conversational history. MC-EIU is enabling technology for many human-computer interfaces. We propose an MC-EIU dataset, which features 7 emotion categories, 9 intent categories, 3 modalities, i.e., textual, acoustic, and visual content, and two languages, English and Mandarin.
  arXiv  Detail & Related papers  (2024-07-03T01:56:00Z)
• Utilizing deep learning models for the identification of enhancers and super-enhancers based on genomic and epigenomic features [0.0]
  This paper provides an extensive examination of a sizable dataset of English tweets focusing on nine widely recognized cryptocurrencies. Our primary objective was to conduct a psycholinguistic and emotion analysis of social media content associated with these cryptocurrencies. The study involved comparing linguistic characteristics across the diverse digital coins, shedding light on the distinctive linguistic patterns that emerge within each coin's community.
  arXiv  Detail & Related papers  (2024-01-15T04:58:50Z)
• Effective Illicit Account Detection on Large Cryptocurrency MultiGraphs [16.25273745598176]
  Rise in cryptocurrency-related illicit activities has led to significant losses for users. Current detection methods mainly depend on feature engineering or are inadequate to leverage the complex information within cryptocurrency transaction networks. We present DIAM, an effective method for detecting illicit accounts in cryptocurrency transaction networks modeled by directed multi-graphs with attributed edges.
  arXiv  Detail & Related papers  (2023-09-04T09:01:56Z)
• AI Ethics on Blockchain: Topic Analysis on Twitter Data for Blockchain Security [0.7251305766151019]
  Existing research recognizes MEV as a severe security issue and proposes potential solutions, including prominent Flashbots. We applied natural language processing (NLP) methods to comprehensively analyze topics in tweets on MEV. Our results show that the tweets discussed profound topics of ethical concern, including security, equity, emotional sentiments, and the desire for solutions to MEV.
  arXiv  Detail & Related papers  (2022-12-14T00:04:56Z)
• Over-communicate no more: Situated RL agents learn concise communication protocols [78.28898217947467]
  It is unclear how to design artificial agents that can learn to effectively and efficiently communicate with each other. Much research on communication emergence uses reinforcement learning (RL) We explore situated communication in a multi-step task, where the acting agent has to forgo an environmental action to communicate. We find that while all tested pressures can disincentivise over-communication, situated communication does it most effectively and, unlike the cost on effort, does not negatively impact emergence.
  arXiv  Detail & Related papers  (2022-11-02T21:08:14Z)
• Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems [51.6210785955659]
  Communication is important in many multi-agent reinforcement learning (MARL) problems for agents to share information and make good decisions. However, when deploying trained communicative agents in a real-world application where noise and potential attackers exist, the safety of communication-based policies becomes a severe issue that is underexplored. In this work, we consider an environment with $N$ agents, where the attacker may arbitrarily change the communication from any $CfracN-12$ agents to a victim agent.
  arXiv  Detail & Related papers  (2022-06-21T07:32:18Z)
• A Mixture-of-Expert Approach to RL-based Dialogue Management [56.08449336469477]
  We use reinforcement learning to develop a dialogue agent that avoids being short-sighted (outputting generic utterances) and maximizes overall user satisfaction. Most existing RL approaches to DM train the agent at the word-level, and thus, have to deal with aly complex action space even for a medium-size vocabulary. We develop a RL-based DM using a novel mixture of expert language model (MoE-LM) that consists of (i) a LM capable of learning diverse semantics for conversation histories, (ii) a number of specialized LMs (or experts) capable of generating utterances corresponding to a
  arXiv  Detail & Related papers  (2022-05-31T19:00:41Z)
• Blockchains through ontologies: the case study of the Ethereum ERC721 standard in OASIS (Extended Version) [0.0]
  This paper reports on how to leverage the emphOntology for Agents, Systems, and Integration of Services as a general means for the semantic representation of smart contracts stored on blockchain as software agents. Special attention is paid to non-fungible tokens (NFTs), whose management through the ERC721 standard is presented as a case study.
  arXiv  Detail & Related papers  (2021-09-07T06:54:11Z)
• Few-shot Language Coordination by Modeling Theory of Mind [95.54446989205117]
  We study the task of few-shot $textitlanguage coordination$. We require the lead agent to coordinate with a $textitpopulation$ of agents with different linguistic abilities. This requires the ability to model the partner's beliefs, a vital component of human communication.
  arXiv  Detail & Related papers  (2021-07-12T19:26:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.