Blockchain Papers

Anna Klimenko, Iakov Korovin

No abstract is available for this record.

Ahmet Faruk Aysan, Ali Yavuz Polat, Hasan Tekin, Ahmet Semih Tunalı

This study aims to investigate the effect of fear sentiment with a novel data set on Bitcoin’s return, volatility and transaction volume. We divide the sample into two subperiods in order to capture the changing dynamics during the COVID-19 pandemic. We retrieve the novel fear sentiment data from Thomson Reuters MarketPsych Indices (TRMI). We denote the subperiods as pre- and post-COVID-19 considering January 13th, 2020, when first COVID-19 confirmed case was reported outside China. We employ bivariate vector autoregressive (VAR) models given below with lag-length k, to investigate the dynamics between Bitcoin variables and fear sentiment. Bitcoin market measures have dissimilar dynamics before and after the Coronavirus outbreak. The results reveal that due to the excessive uncertainty led by the outbreak, an increase in fear sentiment negatively affects the Bitcoin returns more persistently and significantly. For the post-COVID-19 period, an increase in fear also results in more fluctuations in transaction volume while its initial and cumulative effects are both negative. Due to extreme uncertainty caused by the COVID-19 pandemic, investors may trade more aggressively in the initial phases of the shock.

Zeinab Shahbazi, Yung-Cheol Byun

Social media network is one of the important parts of human life based on the recent technologies and developments in terms of computer science area. This environment has become a famous platform for sharing information and news on any topics and daily reports, which is the main era for collecting data and data transmission. There are various advantages of this environment, but in another point of view there are lots of fake news and information that mislead the reader and user for the information needed. Lack of trust-able information and real news of social media information is one of the huge problems of this system. To overcome this problem, we have proposed an integrated system for various aspects of blockchain and natural language processing (NLP) to apply machine learning techniques to detect fake news and better predict fake user accounts and posts. The Reinforcement Learning technique is applied for this process. To improve this platform in terms of security, the decentralized blockchain framework applied, which provides the outline of digital contents authority proof. More specifically, the concept of this system is developing a secure platform to predict and identify fake news in social media networks.

Jiwen Fang, Dickson K.W. Chiu, Kevin K.W. Ho

Social media has become a popular communication platform and aggregated mass information for sentimental analysis. As cryptocurrency has become a hot topic worldwide in recent years, this chapter explores individuals' behavior in sharing Bitcoin information. First, Python was used for extracting around one month's set of Tweet data to obtain a dataset of 11,674 comments during a month of a substantial increase in Bitcoin price. The dataset was cleansed and analyzed by the process documents operator of RapidMiner. A word-cloud visualization for the Tweet dataset was generated. Next, the clustering operator of RapidMiner was used to analyze the similarity of words and the underlying meaning of the comments in different clusters. The clustering results show 85% positive comments on investment and 15% negative ones to Bitcoin-related tweets concerning security. The results represent the generally bullish environment of the cryptocurrency market and general user satisfaction during the period concerned.

Ashutosh Dhar Dwivedi, Rajani Singh, Sakshi Dhall, Gautam Srivastava · 5 authors

In the news industry, as well as in social media, fake news detection and identification of news sources has become a central topic of discussion. In the era of digitization, anyone can easily generate or manipulate digital content and publish them on social media websites. On the one hand, these social networking platforms provide ample ease in modern-day communication but on the other hand, using such platforms has posed new challenges to real-world implementation like viral spreading of false/fake information with malicious intentions. In this paper, a naive blockchain and watermarking based social media framework is proposed to control the fake news propagation. We postulate a new blockchain model to mitigate existing challenges in this field. Moreover, the novel solution can help in reducing the spread of fake news by tracing the root or origin of the fake news on social media. Through our experimental results, we show that our blockchain-based solution is able to immediately stream data through a bloXroute server that can propagate data up to 100 times faster than conventional solutions.

Panayiotis Christodoulou, Klitos Christodoulou

Continuous efforts are being made daily by organizations to develop special tools that can support the collection and organization of available information in order to prevent the spread of fake news. Nowadays, misinformation has been deemed as a great challenge especially with the raise of social media networks that are used as platforms for disseminating information in a digital form. This paper presents the implementation of a decentralized application deployed on the Ethereum blockchain, to be used as a tool for combating fake news and misinformation. The develop framework is proposed as a solution for publishing reliable news sources and enabling readers to self-verify the trustworthiness of the published source. An experimental scenario has been implemented that presents the effectiveness of the proposed framework.

Gowri Ramachandran, Dániel Németh, David M. Neville, Dimitrii Zhelezov · 7 authors

The vast majority of the population is consuming news from various digital sources, including social networking applications such as Twitter and Facebook and other online digital platforms. Such Internet platforms provide malicious entities an opportunity to spread fake news and hoaxes to mislead the population. Besides, Internet users may start to form an opinion and make certain personal or business decisions based on misinformation, leading to undesirable consequences. This paper introduces WhistleBlower, a decentralized and open platform based on the blockchain and distributed ledger technology (DLT) for spotting fake news. The key components of WhistleBlower include a fake news processing engine powered by Artificial Intelligence (AI)/Machine Learning (ML) algorithms, a verifiable computation engine, and a token-curated registry (TCR). WhistleBlower allows the community members to participate in the fake news identification process by running the fake news detection algorithm on their nodes, which would then be validated by a verifiable computation engine to ensure that the public nodes executed the computation honestly and correctly. Whenever a news feed is submitted to WhistleBlower for fake news assessment, it issues a genuineness score, which can then be posted along with the news article to let the newsreaders gauge its legitimacy. However, the genuineness score's accuracy depends on the machine learning model's effectiveness that processes the news item. To improve the machine learning algorithm's reliability, we introduce a Token-curated registry, which enables the public and community members to challenge the algorithm used to estimate the genuineness score. TCR lets the community curate fake news detection algorithms by providing feedback to the ML/AI algorithm developers through the token-curated content moderation process. WhistleBlower is the first open and democratic fake news assessment platform that combines ML/AI, verifiable computation, and TCR to the best of our knowledge.

Prashansa Agrawal, Parwat Singh Anjana, Sathya Peri

The surge in the spread of misleading information, lies, propaganda, and false facts, frequently known as fake news, raised questions concerning social media's influence in today's fast-moving democratic society. The widespread and rapid dissemination of fake news cost us in many ways. For example, individual or societal costs by hampering elections integrity, significant economic losses by impacting stock markets, or increases the risk to national security. It is challenging to overcome the spreading of fake news problems in traditional centralized systems. However, Blockchain-- a distributed decentralized technology that ensures data provenance, authenticity, and traceability by providing a transparent, immutable, and verifiable transaction records can help in detecting and contending fake news. This paper proposes a novel hybrid model DeHiDe: Deep Learning-based Hybrid Model to Detect Fake News using Blockchain. The DeHiDe is a blockchain-based framework for legitimate news sharing by filtering out the fake news. It combines the benefit of blockchain with an intelligent deep learning model to reinforce robustness and accuracy in combating fake news's hurdle. It also compares the proposed method to existing state-of-the-art methods. The DeHiDe is expected to outperform state-of-the-art approaches in terms of services, features, and performance.

Jedidiah Carlson, Kelley Harris

Engagement with scientific manuscripts is frequently facilitated by Twitter and other social media platforms. As such, the demographics of a paper's social media audience provide a wealth of information about how scholarly research is transmitted, consumed, and interpreted by online communities. By paying attention to public perceptions of their publications, scientists can learn whether their research is stimulating positive scholarly and public thought. They can also become aware of potentially negative patterns of interest from groups that misinterpret their work in harmful ways, either willfully or unintentionally, and devise strategies for altering their messaging to mitigate these impacts. In this study, we collected 331,696 Twitter posts referencing 1,800 highly tweeted bioRxiv preprints and leveraged topic modeling to infer the characteristics of various communities engaging with each preprint on Twitter. We agnostically learned the characteristics of these audience sectors from keywords each user's followers provide in their Twitter biographies. We estimate that 96% of the preprints analyzed are dominated by academic audiences on Twitter, suggesting that social media attention does not always correspond to greater public exposure. We further demonstrate how our audience segmentation method can quantify the level of interest from nonspecialist audience sectors such as mental health advocates, dog lovers, video game developers, vegans, bitcoin investors, conspiracy theorists, journalists, religious groups, and political constituencies. Surprisingly, we also found that 10% of the preprints analyzed have sizable (>5%) audience sectors that are associated with right-wing white nationalist communities. Although none of these preprints appear to intentionally espouse any right-wing extremist messages, cases exist in which extremist appropriation comprises more than 50% of the tweets referencing a given preprint. These results present unique opportunities for improving and contextualizing the public discourse surrounding scientific research.

Franklin Tchakounté, Koudanbe Amadou Calvin, Ado Adamou Abba Ari, David Jaurès Fotsa-Mbogne

One of the main concerns of cybersecurity is the detection of hoaxes across social media. Hoaxers propagate such messages to mislead users and to promote violence. Several approaches exist in literature to address this issue. They are mainly limited to detect hoax activities by characterizing the message nature and detecting provenance of messages. However, unless hoaxes are detected, they continue to propagate across social media nodes. This work aims at reducing the dissemination of hoaxes across group of users. Relying on social graph structure, this research develops a mechanism based on smart contract logics to prevent a group to consume a fake post. To achieve this objective, we used a smart contract to exploit a trust index computed based on message characteristics and group features such as graph density, group status, group degree, group acceptability. Based on the value of trust index, the message is forwarded or blocked. Experiments realized on groups of different characteristics revealed that the proposed smart contract is even able to reactively block a fake post of the same nature than the group type. Results indicate that the proportion of targeted groups could be reduced even if their interests match with the message subject. This research is an important step forward to anti-promote hoaxes with the novelty of exploiting smart contract approach to contain their propagation.

Peter A. Chow-White, Alberto Lusoli, Vu Thuy Anh Phan, Sandy Edward Green

Blockchain is one of the most widely debated technologies in recent years. Pundits and scholars have described it as a disruptive technology that will impact many sectors of society. Skeptics argue blockchain’s popularity is fuelled by the media’s obsession for the ‘next big thing’ rather than the intrinsic potential of the technology. In this paper, we follow a social constructivist approach with the aim of explaining how different discourses are creating new meanings about this technology. As Communication scholars, we focus on the role media play in framing debates about blockchain. Our analysis relies on a human coding of the most popular news about blockchain circulating on Twitter from October 2014 to July 2018. The findings show the general attitude about blockchain is predominantly positive. The discourses developing around crypto technologies are complex and multifaceted and indicate a general transition in the rhetorical definition of blockchain.

Taehyun Kim, Hyomin Shin, Hyung Ju Hwang, Seungwon Jeong

Steemit is a blockchain-based social media platform, where authors can get author rewards in the form of cryptocurrencies called STEEM and SBD (Steem Blockchain Dollars) if their posts are upvoted. Interestingly, curators (or voters) can also get rewards by voting others' posts, which is called a curation reward. A reward is proportional to a curator's STEEM stakes. Throughout this process, Steemit hopes "good" content will be automatically discovered by users in a decentralized way, which is known as the Proof-of-Brain (PoB). However, there are many bot accounts programmed to post automatically and get rewards, which discourages real human users from creating good content. We call this type of bot a posting bot. While there are many papers that studied bots on traditional centralized social media platforms such as Facebook and Twitter, we are the first to study posting bots on a blockchain-based social media platform. Compared with the bot detection on the usual social media platforms, the features we created have an advantage that posting bots can be detected without limiting the number or length of posts. We can extract the features of posts by clustering distances between blog data or replies. These features are obtained from the Minimum Average Cluster from Clustering Distance between Frequent words and Articles (MAC-CDFA), which is not used in any of the previous social media research. Based on the enriched features, we enhanced the quality of classification tasks. Comparing the F1-scores, the features we created outperformed the features used for bot detection on Facebook and Twitter.

Qian Chen, Gautam Srivastava, Reza M. Parizi, Moayad Aloqaily · 5 authors

No abstract is available for this record.

Pengcheng Xia, Haoyu Wang, Xiapu Luo, Lei Wu · 9 authors

As COVID-19 has been spreading across the world since early 2020, a growing number of malicious campaigns are capitalizing the topic of COVID-19. COVID-19 themed cryptocurrency scams are increasingly popular during the pandemic. However, these newly emerging scams are poorly understood by our community. In this paper, we present the first measurement study of COVID-19 themed cryptocurrency scams. We first create a comprehensive taxonomy of COVID-19 scams by manually analyzing the existing scams reported by users from online resources. Then, we propose a hybrid approach to perform the investigation by: 1) collecting reported scams in the wild; and 2) detecting undisclosed ones based on information collected from suspicious entities (e.g., domains, tweets, etc). We have collected 195 confirmed COVID-19 cryptocurrency scams in total, including 91 token scams, 19 giveaway scams, 9 blackmail scams, 14 crypto malware scams, 9 Ponzi scheme scams, and 53 donation scams. We then identified over 200 blockchain addresses associated with these scams, which lead to at least 330K US dollars in losses from 6,329 victims. For each type of scams, we further investigated the tricks and social engineering techniques they used. To facilitate future research, we have released all the well-labelled scams to the research community.

Zakwan Jaroucheh, Mohamad Alissa, William J. Buchanan, Xiaodong Liu

The growing trend of sharing news/contents, through social media platforms and the World Wide Web has been seen to impact our perception of the truth, altering our views about politics, economics, relationships, needs and wants. This is because of the growing spread of misinformation and disinformation intentionally or unintentionally by individuals and organizations. This trend has grave political, social, ethical, and privacy implications for society due to 1) the rapid developments in the field of Machine Learning (ML) and Deep Learning (DL) algorithms in creating realistic-looking yet fake digital content (such as text, images, and videos), 2) the ability to customize the content feeds and to create a polarized so-called "filter-bubbles" leveraging the availability of the big-data. Therefore, there is an ethical need to combat the flow of fake content. This paper attempts to resolves some of the aspects of this combat by presenting a high-level overview of TRUSTD, a blockchain and collective signature based ecosystem to help content creators in getting their content backed by the community, and to help users judge on the credibility and correctness of these contents.

Piotr Stolarski, Włodzimierz Lewoniewski, Witold Abramowicz

In this research we presented different approaches to investigate the possible relationships between the largest crowd-based knowledge source and the market potential of particular cryptocurrencies. Identification of such relations is crucial because their existence may be used to create a broad spectrum of analyses and reports about cryptocurrency projects and to obtain a comprehensive outlook of the blockchain domain. The activities on the blockchain reach different levels of anonymity which renders them hard objects of studies. In particular, the standard tools used to characterize social trends and variables that describe cryptocurrencies’ situations are unsuitable to be used in the environment that extensively employs cryptographic techniques to hide real users. The employment of Wikipedia to trace crypto assets value need examination because the portal allows gathering of different opinions—content of the articles is edited by a group of people. Consequently, the information can be more attractive and useful for the readers than in case of non-collaborative sources of information. Wikipedia Articles often appears in the premium position of such search engines as Google, Bing, Yahoo and others. One may expect different demand on information about particular cryptocurrency depending on the different events (e.g., sharp fluctuations of price). Wikipedia offers only information about cryptocurrencies that are important from the point of view of language community of the users in Wikipedia. This “filter” helps to better identify those cryptocurrencies that have a significant influence on the regional markets. The models encompass linkages between different variables and properties. In one model cryptocurrency projects are ranked with the means of articles sentiment and quality. In another model, Wikipedia visits are linked to cryptocurrencies’ popularity. Additionally, the interactions between information demand in different Wikipedia language versions are elaborated. They are used to assess the geographical esteem of certain crypto coins. The information about the legal status of cryptocurrency technologies in different states that are offered by Wikipedia is used in another proposed model. It allows assessment of the adoption of cryptocurrencies in a given legislature. Finally, a model is developed that joins Wikipedia articles editions and deletions with the social sentiment towards particular cryptocurrency projects. The mentioned analytical purposes that permit assessment of the popularity of blockchain technologies in different local communities are not the only results of the paper. The models can show which country has the biggest demand on particular cryptocurrencies, such as Bitcoin, Ethereum, Ripple, Bitcoin Cash, Monero, Litecoin, Dogecoin and others.

Harsh Salvi

With the revolution and growth of the media industry, and development of new mediums to update citizens with the latest news, in recent years there has been a spurt in the production of articles spreading fake information. Many media channels leverage on the concept of spreading eye-catching malicious news that attracts readers which has been proven to be quite dangerous in most cases. These channels post an exaggerated version of the truth, thus leading to an emerging trend of spreading fake news. To tackle this problem, we propose a two-step solution involving machine learning and block chain. The proposed solution consists of a news verification portal using a two-fold approach, which first detects whether the news article is fake or real leveraging the accuracy of a machine learning algorithm and then verifies the source using human crowd auditors on a block chain platform based on proof-of-stake.

Diogo Pacheco, Pik-Mai Hui, Christopher Torres-Lugo, Bao Tran Truong · 6 authors

Coordinated campaigns are used to influence and manipulate social media platforms and their users, a critical challenge to the free exchange of information online. Here we introduce a general, unsupervised network-based methodology to uncover groups of accounts that are likely coordinated. The proposed method constructs coordination networks based on arbitrary behavioral traces shared among accounts. We present five case studies of influence campaigns, four of which in the diverse contexts of U.S. elections, Hong Kong protests, the Syrian civil war, and cryptocurrency manipulation. In each of these cases, we detect networks of coordinated Twitter accounts by examining their identities, images, hashtag sequences, retweets, or temporal patterns. The proposed approach proves to be broadly applicable to uncover different kinds of coordination across information warfare scenarios.

Safi Ur Rehman, Muhammad Usman Shahid Khan, Mazhar Ali

The blockchain is an intelligent and cryptographically secure technology. Blockchain provides data integrity, security, and anonymity without any third-party enabling immutable database technology with a built-in trust mechanism. In this paper, we focused on utilizing blockchain technology for proving the origin of digital media by enhancing the functionality of an existing solution. This research improves the functionality of the existing solution by using perceptual hashing. Perceptual hashing ables to differentiate similar digital media and ensures the explicit relationship between the digital media and the content creator. Experimental results show that perceptual hashing gives better performance in the detection of image alteration and sensitive to small changes.

Karen Watts DiCicco, Nitin Agarwal

No abstract is available for this record.

Leonardo Nizzoli, Serena Tardelli, Marco Avvenuti, Stefano Cresci · 6 authors

Cryptocurrencies represent one of the most attractive markets for financial speculation. As a consequence, they have attracted unprecedented attention on social media. Besides genuine discussions and legitimate investment initiatives, several deceptive activities have flourished. In this work, we chart the online cryptocurrency landscape across multiple platforms. To reach our goal, we collected a large dataset, composed of more than 50M messages published by almost 7M users on Twitter, Telegram and Discord, over three months. We performed bot detection on Twitter accounts sharing invite links to Telegram and Discord channels, and we discovered that more than 56% of them were bots or suspended accounts. Then, we applied topic modeling techniques to Telegram and Discord messages, unveiling two different deception schemes - “pump-and-dump” and “Ponzi” - and identifying the channels involved in these frauds. Whereas on Discord we found a negligible level of deception, on Telegram we retrieved 296 channels involved in pump-and-dump and 432 involved in Ponzi schemes, accounting for a striking 20% of the total. Moreover, we observed that 93% of the invite links shared by Twitter bots point to Telegram pump-and-dump channels, shedding light on a little-known social bot activity. Charting the landscape of online cryptocurrency manipulation can inform actionable policies to fight such abuse.

Luís Filipe Sobral Silva

We live in an increasingly digital world, where Smart Cities have become a reality. One of the characteristics that make these cities smart is their ability to gather information and act upon it, improving their citizens lives. In this work, we present our system, Dignitas. A blockchain-based reputation system that allows citizens of a Smart City to assess the truthiness of information posted by other citizens. This assessment is based on a bet that reporters make, and all of those who agreed with him, that puts their gathered reputation at stake. This use of Reputation as a currency is a novel idea that allowed us to build an anonymous system. Using blockchain we were able to have multiple authorities, working with each other to make the system secure and thus avoiding centralized schemes. Our work was focused on developing our idea, a proof of concept, and testing the viability of our new solution.

Shane J. Ralston

No abstract is available for this record.

Andrew Burnie, Emine Yılmaz

We develop a new Data-Driven Phasic Word Identification (DDPWI) methodology to determine which words matter as the bitcoin pricing dynamic changes from one phase to another. With Google search volumes as a baseline, we find that Reddit submissions are both correlated with Google and have a comparable relationship with a variety of bitcoin metrics, using Spearman's rho. Reddit provides complete access to the text of submissions. Rather than associating sentiment with market activity, we describe the DDPWI method for finding specific 'price dynamic' words associated with changes in the bitcoin pricing pattern through 2017 and 2018. We assess the significance of these changes using Wilcoxon Rank-Sum Tests with Bonferroni corrections. These price dynamic words are used to pull out associated words in the submissions thereby providing the context to their use. For example, the price dynamic word 'ban', which became significantly higher in frequency as prices fell, occurred in the context of both government regulation and internet companies banning cryptocurrency adverts. This approach could be used more generally to look at social media and discussion forums at a granular level identifying specific words that impact the metric under investigation rather than overall sentiment.