Public software repositories such as GitHub make transparent the development history of an open source software system. Source code commits, discussions about new features and bugs, and code reviews are stored and carefully attributed to the appropriate developers. However, sometimes governments may seek to analyze these repositories, to identify citizens who contribute to projects they disapprove of, such as those involving cryptography or social media. While developers who seek anonymity may contribute under assumed identities, their body of public work may be characteristic enough to betray who they really are. The ability to contribute anonymously to public bodies of knowledge is extremely important to the future of technological and intellectual freedoms. Just as in security hacking, the only way to protect vulnerable individuals is by demonstrating the means and strength of available attacks so that those concerned may know of the need and develop the means to protect themselves. \n \nIn this work, we present a method to de-anonymize source code contributors based on the authors' intrinsic programming style. First, we present a partial replication study wherein we attempt to de-anonymize a large number of entries into the Google Code Jam competition. We base our approach on Caliskan-Islam et al. 2015, but with modifications to the feature set and modelling strategy for scalability and feature-selection robustness. We did not achieve 0.98 F1 achieved in this prior work, but managed a still reasonable 0.71 F1 under identical experimental conditions, and a 0.88 F1 given more data from the same set. \n \nSecond, we present an exploratory study focused on de-anonymizing programmers who have contributed to a repository, using other commits from the same repository as training data. We train random-forest classifiers using programmer data collected from 37 medium to large open-source repositories. Given a choice between active developers in a project, we were able to correctly determine authorship of a given function about 75% of the time, without the use of identifying meta-data or comments. We were also able to correctly validate a contributor as the author of a questioned function with 80\\% recall and 65\\% precision. This exploratory study provides empirical support for our approach. \n \nFinally, we present the results of a similar, but more difficult study wherein we attempt de-anonymize a repository in the same manner, but without using the target repository as training data. To do this, we gather as much training data as possible from the repository's contributors through the Github API. We evaluate our technique over 3 repositories: Bitcoin, Ethereum (crypto-currencies) and TrinityCore (a game engine). Our results in this experiment starkly contrast our results in the intra-repository study showing accuracies of 35% for Bitcoin, 22% for Ethereum, and 21% for TrinityCore which had candidate set sizes of 6, 5, and 7 respectively. \n \nOur results indicate that we can do somewhat better than random guessing, even under difficult experimental conditions, but they also indicate some fundamental issues with the state of the art of Code Stylometry. In this work we present our methodology, results, and some comments on past empirical studies, the difficulties we faced, and likely hurdles for future work in the area.
An interdisciplinary effort is needed for solving the fake news crisis, because the solutions depend not only on AI, but also on social mechanisms. In this paper, we propose an AI blockchain platform to build a strong collaboration among AI blockchain researchers and news media to advance the research fighting against fake news. This platform will provide journalists with blockchain crowd-sourced and AI validated factual data on emerging news. This platform will gather blockchain traced data and AI tools that can provide pointers to the original data sources, news propagation path, AI analyzed experts to consult on a given topic. This will provide journalists with cheaper and reliable sources of information in the Internet social media age. So that factual-sourced reporting can outpace the spread of fake news on social media which will encourage factual news sources as a way to value and promote truth for society. The technical contributions of this paper are (1) mechanism building the factual news database, (2) mechanism generating the news blockchain supply chain graph, and (3) AI blockchain based crowd sourcing fake news ranking mechanisms (4) AI blockchain platform for trusting news ecosystem. (5) reviewing the state of fake news research from the technology and social aspects, and providing list of key research issues and technical challenges.
Fake news has become a major problem in the cyberspace with far reaching consequences. The open access and unregulated social networks are popular attack vectors that are frequently used to propagate misinformation. To fight this problem, several naïve solutions have been proposed, including a blockchain implementation of news feed to distinguish facts from fiction. However, the size of social networks and the design constructs of blockchain add several new challenges that impede the real world deployment of such solutions. In this paper, we postulate a new blockchain system that overcomes the existing challenges and limits the spread of fake news across the network. Towards that, we analyze the information workflow in the social networks and construct an optimal detection system that can be effectively deployed with minimal overhead. Moreover, our proposed solution can be extended beyond social networks to other online platforms.
Blockchain technology has generated a great deal of interest in recent years, as has the associated area of cryptocurrency trading, not only on the part of individuals but also from traditional financial institutions and hedge funds. However, there is currently limited knowledge as to how to predict future cryptocurrency price movements. This thesis investigates whether online indicators, especially from social media, can be harnessed to predict cryptocurrency price movements – to achieve this, three experiments are conducted. The first experiment analyses time-evolving relationships between chosen online indicators and associated cryptocurrency prices; relationships are considered over short, medium and long-term durations. The work introduces and evaluates several influential factors from the social media platform Reddit, a platform previously unexplored within cryptocurrency prediction literature. It is found that medium and longer-term relationships strengthen in bubble market regimes (compared to non-bubble regimes). The second experiment utilises these promising new factors as inputs to a predictive model. The model used was originally designed to detect influenza epidemic outbreaks, and is repurposed here to model epidemic-like cryptocurrency price bubbles, demonstrating how social media can be used to track the epidemic spread of an investment idea. The predictive power of the model is validated through the generation of a profitable trading strategy. Having considered quantitative count-based metrics in the previous chapters (e.g. posts per day, submissions per day, new authors per day etc.), the next experiment considers the content of social media submissions. More specifically, the third experiment analyses social media submission content to investigate whether certain topics of discussion precede upcoming shorter term (positive or negative) price movements. Information evidencing time-varying interest in various topics is retrieved from social media submissions, upon which hidden interactions with the associated cryptocurrency price are deciphered. It is found that certain topics precede major positive or negative price movements, and also additional analysis shows that certain discussion topics exhibit longer-term relationships with cryptocurrency market prices.
The rise of ubiquitous deepfakes, misinformation, disinformation, propaganda and post-truth, often referred to as fake news, raises concerns over the role of Internet and social media in modern democratic societies. Due to its rapid and widespread diffusion, digital deception has not only an individual or societal cost (e.g., to hamper the integrity of elections), but it can lead to significant economic losses (e.g., to affect stock market performance) or to risks to national security. Blockchain and other Distributed Ledger Technologies (DLTs) guarantee the provenance, authenticity and traceability of data by providing a transparent, immutable and verifiable record of transactions while creating a peer-to-peer secure platform for storing and exchanging information. This overview aims to explore the potential of DLTs and blockchain to combat digital deception, reviewing initiatives that are currently under development and identifying their main current challenges. Moreover, some recommendations are enumerated to guide future researchers on issues that will have to be tackled to face fake news, disinformation and deepfakes, as an integral part of strengthening the resilience against cyber-threats on today's online media.
Adnan Qayyum, Junaid Qadir, Muhammad Umar Janjua, Falak Sher
In recent years, `fake news' has become a global issue that raises unprecedented challenges for human society and democracy. This problem has arisen due to the emergence of various concomitant phenomena such as (1) the digitization of human life and the ease of disseminating news through social networking applications (such as Facebook and WhatsApp); (2) the availability of `big data' that allows customization of news feeds and the creation of polarized so-called `filter-bubbles'; and (3) the rapid progress made by generative machine learning (ML) and deep learning (DL) algorithms in creating realistic-looking yet fake digital content (such as text, images, and videos). There is a crucial need to combat the rampant rise of fake news and disinformation. In this paper, we propose a high-level overview of a blockchain-based framework for fake news prevention and highlight the various design issues and consideration of such a blockchain-based framework for tackling fake news.
Trust in a smart city is fundamental to its transparency, the participation of its people in governance, entrepreneurial initiatives, trade, commerce and hence the growth of its economy. A city gets smart by transforming itself to a digital city, and a digital city runs on data, analytics, internet of things, artificial intelligence and machine learning. This inevitable transformation creates the fundamental need for trust. How does data remain sacrosanct and verifiable? How do people trust institutions? How do institutions trust each other? How do devices trust each other? This article explores blockchain as an essential layer of trust in a smart city. It explains the technology by drawing real-life examples to the ‘memory game’ that operates in an ecosystem of ‘trust and consensus.’ The article provides further insight into institutions that can be governed on blockchain through ‘smart contracts’ in a sovereign and human independent manner. The use cases of blockchain have been corroborated with examples of successful blockchain implementation. The value for blockchain, in general, and smart cities, in particular, has been presented across four categories: (a) the network effect on trust on society, governments and industries; (b) empowering the individual and strengthening the economy; (c) the liquid economy and (d) the shareable economy. Given the current topology of technology innovations, there is no solution better than blockchain that embodies trust. It is a hope and expectation that this article will help smart city planners, developers, architects and thinkers implement blockchain as the embodiment of trust in smart cities that are increasingly becoming digital.
Die Unsicherheit über den intrinsischen Wert von Kryptowährungen und der nachgewiesene Einfluss der Aufmerksamkeit an dem Marktwert verschiedener Vermögenswerte haben uns veranlasst, den Einfluss der Aufmerksamkeit auf den Marktwert von Kryptowährungen zu untersuchen. Als Aufmerksamkeitsindikator haben wir das Volumen von Google-Suchen zu bestimmten der Suchwörter Suchbegriffe genutzt das Suchvolumen von Google-Suchmenge, die auf Schlüsselwörtern basieren, die sich auf unseren Satz von Kryptowährungen einer sehr genauen Granularität beziehen. Unter Verwendung von ARMA und VECM haben wir getestet, ob die Google-Suchmenge die Vorhersage für Kryptowährungspreisentwicklung im Zeitrahmen von 15 Minuten bis zu einem Tag verbessert. Anschließend haben wir den Handel mit dieser Out-of-Sample Prognose simuliert und kamen zu dem Schluss, dass im Fall von häufigem Handel ohne Gebühren, einfache, univariate, autoregressive Modelle besser Ergebnisse produzieren. Unter Vernachlässigung von Gebühren jedoch, verbessert sich durch die Einbeziehung der Variablen für das Google-Suchvolumen das Handelsergebnisse, insbesondere bei stündlichen und täglichen Frequenzen. Unter Verwendung solcher Frequenzen übertraf das Modell univariate Modelle sowie das Wachstum der zugrunde liegenden Vermögenswerte.
Mehrnoosh Mirtaheri, Sami Abu-El-Haija, Fred Morstatter, Greg Ver Steeg · 5 authors
Interest surrounding cryptocurrencies, digital or virtual currencies that are used as a medium for financial transactions, has grown tremendously in recent years. The anonymity surrounding these currencies makes investors particularly susceptible to fraud---such as ``pump and dump'' scams---where the goal is to artificially inflate the perceived worth of a currency, luring victims into investing before the fraudsters can sell their holdings. Because of the speed and relative anonymity offered by social platforms such as Twitter and Telegram, social media has become a preferred platform for scammers who wish to spread false hype about the cryptocurrency they are trying to pump. In this work we propose and evaluate a computational approach that can automatically identify pump and dump scams as they unfold by combining information across social media platforms. We also develop a multi-modal approach for predicting whether a particular pump attempt will succeed or not. Finally, we analyze the prevalence of bots in cryptocurrency related tweets, and observe a significant increase in bot activity during the pump attempts.
Rumors and misleading information detection and prevention still represent a big challenge against social network developers and researchers. Since newsworthy information propagation is a traditional behavior of most of the users in social media, then verifying information credibility and reliability is indeed a vital security requirement for social network platforms. Due to its immutability, security, tamper-proof and P2P design, Blockchain as a powerful technology can provide a magical solution to overcome this challenge. This Paper introduces a novel blockchain approach called Proof of Credibility (PoC) for detecting fake news and blocking its propagation in social networks. The functionality of the PoC protocol has been simulated on two datasets of newsworthy tweets collected from different news sources on Twitter. The results clarified a satisfying performance and efficiency of the proposed approach in detecting rumors and blocking its propagation.
Gyuwon Song, Suhyun Kim, Hae‐Jin Hwang, Kwanhoon Lee
This paper presents a blockchain-based notarization service for social media. With the advent of smartphones, social media platforms have become an undeniably popular way to communicate with people across the world. However, fake news and maliciously fabricated screenshots are continuously produced and distributed on social media. Since blockchain technology can store data in a secure and tamper-proof way, it is a suitable platform for notarizing online activity. The problem is how we can verify the data to notarize. We propose an architecture that can authentically archive the contents on social media using blockchain technology. Based on the proposed method, an instant messaging scenario is presented as a proof-of-concept.
Johannes Beck, Roberta Huang, David Lindner, Tian Guo · 7 authors
The ability to track and monitor relevant and important news in real-time is of crucial interest in multiple industrial sectors. In this work, we focus on the set of cryptocurrency news, which recently became of emerging interest to the general and financial audience. In order to track relevant news in real-time, we (i) match news from the web with tweets from social media, (ii) track their intraday tweet activity and (iii) explore different machine learning models for predicting the number of the article mentions on Twitter within the first 24 hours after its publication. We compare several machine learning models, such as linear extrapolation, linear and random forest autoregressive models, and a sequence-to-sequence neural network. We find that the random forest autoregressive model behaves comparably to more complex models in the majority of tasks.
In early 2018 prices peaked at USD 20,000 and, almost two years later, we still continue debating if cryptocurrencies can actually become a currency for the everyday life or not. From the economic point of view, and playing in the field of behavioral finance, this paper analyses the relation between prices and the search interest on Bitcoin since 2014. We questioned the forecasting ability of Google Trends for the behavior of price by performing linear and nonlinear dependency tests, and exploring performance of ARIMA and Neural Network models enhanced with this social sentiment indicator. Our analyses and models are founded upon a set of statistical properties common to financial returns that we establish for Bitcoin, Ethereum, Ripple and Litecoin.
Eaman Jahani, P. M. Krafft, Yoshihiko Suhara, Esteban Moro · 5 authors
Participants in cryptocurrency markets are in constant communication with each other about the latest coins and news releases. Do these conversations build hype through the contagiousness of excitement, help the community process information, or play some other role? Using a novel dataset from a major cryptocurrency forum, we conduct an exploratory study of the characteristics of online discussion around cryptocurrencies. Through a regression analysis, we find that coins with more information available and higher levels of technical innovation are associated with higher quality discussion. People who talk about "serious" coins tend to participate in discussion displaying signatures of collective intelligence and information processing, while people who talk about "less serious" coins tend to display signatures of hype and naïvety. Interviews with experienced forum members also confirm these quantitative findings. These results highlight the varied roles of discussion in the cryptocurrency ecosystem and suggest that discussion of serious coins may be oriented towards earnest, perhaps more accurate, attempts at discovering which coins are likely to succeed.
Fake news on major social media platforms has real-world consequences on the sentiments of citizens. For instance, it has the power to influence the election results of a country. The problem statement is fake news detection and prevention on social media presents unique challenges that require novel algorithms. The research methodology is to implement current blockchain technology with advanced Artificial Intelligence in social media platform to prevent fake news. This study aims to provide a substantial review on implementing blockchain on social media in order to build public trust on credible news and prevent spread of fake news via social media. In particular, this paper provides the research problem and discusses state-of-the-art blockchain solutions and technical constraints as well as points out the future research direction in tackling the challenges.
In the context of Bitcoin, we examine the relationship between Bitcoin price movement and social data sentiment. Baseline findings reveal that social media provides value-relevant information in both short-term and long-term predictions. By comparing the predictive power across different information channels and different user groups, we found that (1) while speculative information predicts both long-term and short-term returns effectively, fundamental-related information only predicts long-term returns, and that (2) prediction accuracy is higher for less active users than for active users on social media, especially in long-term prediction.
May 28, 2017·CEUR Workshop Proceedings, Vol-2104: Proceedings of the 14th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Volume II: Workshops
The paper investigates the Bitcoin exchange rate response to the dai- ly Twitter data. Sentiment score is computed for the number of obtained tweets. The prediction accuracy for the Bitcoin exchange rate employing the sentiment score reveals the influence of the Twitter social network on the news diffusion and target exchange rate volatility. We used the historical data on the Bitcoin exchange rate and the daily sentiment score of the pertinent tweets to forecast the direction of change for the Bitcoin. The results show better performance of the developed forecasting method with both historical data on the exchange rate and the sentiment score than using only the exchange rate data as an input.
The Internet pervades modern life, offering up opportunities to connect, inform and be informed. As the range and number of sources for information online explode, how people select and interpret information has become a pertinent area for study, not least in light of the prevalence of fake-news. People are well known to act upon information they believe to be trustworthy and where the decision to act incurs risk, an inability to accurately select and assess the credibility of information presents a challenge. Bitcoin, the nascent crypto-currency, presents a domain within which profound financial risk abounds. Even for those armed with experience and knowledge there are numerous challenges to assessing risk, especially as sources of Bitcoin information can be observed to be partisan and of questionable accuracy. Within the domain of bitcoin speculation, this thesis asks the central research question of: are people able to select and correctly evaluate information they might rely upon to make decisions? In addressing this research question, this thesis offers - through the application of a psychological model of informational trust to bitcoin speculators - two fundamental contributions: Firstly, that these users are able to identify relevant news without a reliance upon confirmation bias. Secondly, that a notable percentage of users are not evaluating the credibility of online news by expertly interpreting the fundamentals of information but, rather deferring their trust to either the source news website or a more broad trust of information on the Internet. For these users, chance or luck may mean that they are basing their decisions upon factually accurate news. But this is a position which makes them particularly vulnerable to fake-news where it is spread via sources which they might trust. This position of susceptibility provides evidence to support further security research of both the prevalence of, and counter-measures for fake-news.
Bitcoin is difficult to categorize and indeed has been associated with 112 different labels in the British media (e.g., “private money,” “commodity”) – most of which poorly describe bitcoin. Specifically, our analyses of 674 media articles, focusing on the relationship between labeling and categorization, identify classification inconsistencies at three levels: within clusters of labels, between labels and categories, and between category attributes. These inconsistencies hamper categorization based on attribute similarity, audience goals, and causal models, respectively. We identify four factors that nurture this categorical anarchy and conclude with a call for research on the socioeconomic revolution heralded by blockchain technology.
Digital currencies represent a new method for exchange and investment that differs strongly from any other fiat money seen throughout history. A digital currency makes it possible to perform all financial transactions without the intervention of a third party to act as an arbiter of verification; payments can be made between two people with degrees of anonymity, across continents, at any denomination, and without any transaction fees going to a central authority. The most successful example of this is Bitcoin, introduced in 2008, which has experienced a recent boom of popularity, media attention, and investment. With this surge of attention, we became interested in finding out how people both inside and outside the Bitcoin community perceive Bitcoin -- what do they think of it, how do they feel, and how knowledgeable they are. Towards this end, we conducted the first interview study (N = 20) with participants to discuss Bitcoin and other related financial topics. Some of our major findings include: not understanding how Bitcoin works is not a barrier for entry, although non-user participants claim it would be for them and that user participants are in a state of cognitive dissonance concerning the role of governments in the system. Our findings, overall, contribute to knowledge concerning Bitcoin and attitudes towards digital currencies in general.