Deep Learning-based Gated Recurrent Unit Approach to Stock Market Forecasting: An Analysis of Intel\u27s Stock Data

The stock price index prediction is a very challenging task that\u27s because the market has a very complicated nonlinear movement system. This fluctuation is influenced by many different factors. Multiple examples demonstrate the suitability of Machine Learning (ML) models like Neural Network algorithms (NN) and Long Short-Term Memory (LSTM) for such time series predictions, as well as how frequently they produce satisfactory outcomes. However, relatively few studies have employed robust feature engineering sequence models to forecast future prices. In this paper, we propose a cutting-edge stock price prediction model based on a Deep Learning (DL) technique. We chose the stock data for Intel, the firm with one of the quickest growths in the past ten years. The experimental results demonstrate that, for predicting this particular stock time series, our suggested model outperforms the current Gated Recurrent Unit (GRU) model. Our prediction approach reduces inaccuracy by taking into account the random nature of data on a big scale

Coordinated Behavior on Social Media in 2019 UK General Election

Coordinated online behaviors are an essential part of information and influence operations, as they allow a more effective disinformation's spread. Most studies on coordinated behaviors involved manual investigations, and the few existing computational approaches make bold assumptions or oversimplify the problem to make it tractable. Here, we propose a new network-based framework for uncovering and studying coordinated behaviors on social media. Our research extends existing systems and goes beyond limiting binary classifications of coordinated and uncoordinated behaviors. It allows to expose different coordination patterns and to estimate the degree of coordination that characterizes diverse communities. We apply our framework to a dataset collected during the 2019 UK General Election, detecting and characterizing coordinated communities that participated in the electoral debate. Our work conveys both theoretical and practical implications and provides more nuanced and fine-grained results for studying online information manipulation.Comment: Version accepted in Proc. AAAI Intl. Conference on Web and Social Media (ICWSM) 2021. Added dataset DO

Old Frauds With a New Sauce: Digital Assets and Space Transition

Purpose The purpose of this study is to describe the evolution of fraud schemes with historically conducted with fiat money in physical space to the crypto-assets in digital space as follows: ransomware, price manipulation, pump and dump schemes, misrepresentation, spoofing and Ponzi Schemes. To explain how fraud schemes have evolved alongside digital asset markets, this study applies the space transition theory. Design/methodology/approach The methodology used is a review of the media regarding six digital asset fraud schemes that have evolved from physical space to virtual space that are currently operational, as well as a review of the literature regarding the space transition theory. Findings This paper finds that the digital space and digital assets may facilitate pseudonymous criminal behavior in the present regulatory environment. Research limitations/implications The field is rapidly evolving, however this study finds that the conversion from physical to virtual space obfuscates the criminal activity, facilitating anonymity of the perpetrators, and creating new challenges for the legal and regulatory environment. Practical implications This paper finds that the digital space and digital assets may facilitate pseudonymous criminal behavior in the present regulatory environment. An understanding of the six crypto-asset fraud schemes described in the paper is useful for anti-financial crime professionals and regulators focusing on deterrence. Social implications The space transition theory offers an explanation for why digital space leads criminals to be better positioned to conduct financial crime in virtual space relative to physical space. This offers insights into behavior of digital asset fraudster behavior that could help limit the social damage caused by crypto-asset fraud. Originality/value To the authors’ knowledge, this paper is the first to detail the evolution of fraud schemes with fiat money in physical space to their corresponding schemes with digital assets in physical space. This study is also the first to integrate the space transition theory into an analysis of digital asset fraud schemes

A Decade of Social Bot Detection

On the morning of November 9th 2016, the world woke up to the shocking outcome of the US Presidential elections: Donald Trump was the 45th President of the United States of America. An unexpected event that still has tremendous consequences all over the world. Today, we know that a minority of social bots, automated social media accounts mimicking humans, played a central role in spreading divisive messages and disinformation, possibly contributing to Trump's victory. In the aftermath of the 2016 US elections, the world started to realize the gravity of widespread deception in social media. Following Trump's exploit, we witnessed to the emergence of a strident dissonance between the multitude of efforts for detecting and removing bots, and the increasing effects that these malicious actors seem to have on our societies. This paradox opens a burning question: What strategies should we enforce in order to stop this social bot pandemic? In these times, during the run-up to the 2020 US elections, the question appears as more crucial than ever. What stroke social, political and economic analysts after 2016, deception and automation, has been however a matter of study for computer scientists since at least 2010. In this work, we briefly survey the first decade of research in social bot detection. Via a longitudinal analysis, we discuss the main trends of research in the fight against bots, the major results that were achieved, and the factors that make this never-ending battle so challenging. Capitalizing on lessons learned from our extensive analysis, we suggest possible innovations that could give us the upper hand against deception and manipulation. Studying a decade of endeavours at social bot detection can also inform strategies for detecting and mitigating the effects of other, more recent, forms of online deception, such as strategic information operations and political trolls.Comment: Forthcoming in Communications of the AC

Rethinking "Risk" in Algorithmic Systems Through A Computational Narrative Analysis of Casenotes in Child-Welfare

Risk assessment algorithms are being adopted by public sector agencies to make high-stakes decisions about human lives. Algorithms model "risk" based on individual client characteristics to identify clients most in need. However, this understanding of risk is primarily based on easily quantifiable risk factors that present an incomplete and biased perspective of clients. We conducted a computational narrative analysis of child-welfare casenotes and draw attention to deeper systemic risk factors that are hard to quantify but directly impact families and street-level decision-making. We found that beyond individual risk factors, the system itself poses a significant amount of risk where parents are over-surveilled by caseworkers and lack agency in decision-making processes. We also problematize the notion of risk as a static construct by highlighting the temporality and mediating effects of different risk, protective, systemic, and procedural factors. Finally, we draw caution against using casenotes in NLP-based systems by unpacking their limitations and biases embedded within them

Understanding public discourse surrounding the impact of bitcoin on the environment in social media

AbstractIncreasing public concerns about the environment have led to many studies that have explored current issues and approaches towards its protection. Much less studied, however, is topic of public opinion surrounding the impact that cryptocurrencies are having on the environment. The cryptocurrency market, in particular, bitcoin, currently rivals other top well-known assets such as precious metals and exchanged traded funds in market value, and its growing. This work examines public opinion expressed about the environmental impacts of bitcoin derived from Twitter feeds. Three primary research questions were addressed in this work related to topics of public interest, their location, and people and places involved. Our findings show that factions of of the public are interest in protecting the environment, with topics that resonate mainly related to energy. This discourse was also taking place at few similar locations with a mix of different people and places of interest.</jats:p

How Crisis Affects Crypto: Coronavirus as a Test Case

Everybody is talking about cryptocurrencies. These digital tokens, which started in a one-asset market, have swiftly ballooned into a massive and diverse “cryptomarket.” The cryptomarket is still mostly unregulated, but this is about to change. With President Biden’s adoption of the Executive Order on Ensuring Responsible Development of Digital Assets, regulatory initiatives are being adopted abroad, and global regulation looms ahead. In light of the expected regulatory changes, two important questions emerge: is there a clear rationale for legal intervention in the cryptomarket? And if so, what type of regulation is optimal? This Article is the first to consider how to regulate the cryptomarket through an empirical analysis of how the COVID-19 crisis affected the cryptomarket. We take a two-step approach to answer these pivotal questions. First, we analyze empirical evidence from the early days of the COVID-19 pandemic to better understand the risks posed by the cryptomarket when a crisis emerges. Second, we apply a law-and-economics approach to identify which market failures are consistent with the data and derive novel regulatory lessons. Our empirical analysis reveals an interesting pattern: investors initially shifted funds to the cryptomarket when the pandemic erupted, but then made a U-turn and diverted funds out of cryptocurrencies, leading to a plunge in the market. We maintain that such investor behavior can have both rational and behavioral explanations, which in turn affects the optimal choice of regulation. Accordingly, we map each rational and behavioral explanation onto potential market failures by surveying different possible interpretations of our findings, such as substitution effects between traditional markets and the cryptomarket, exploitation of investors in the form of pumpand- dump schemes, and other criminal activities. We then discuss how each type of failure can serve as justification for regulation and derive regulatory lessons on how to best intervene in the cryptomarket depending on the source of the market failure

Charting the Landscape of Online Cryptocurrency Manipulation

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