From playing games to committing crimes: A multi-technique approach to predicting key actors on an online gaming forum

We propose a systematic framework for analysing forum datasets, which contain minimal structure, and are non-trivial to analyse at scale, aiming to support future analysis of underground forum communities. We use a multi-technique approach which draws on a combination of features, including post classifications extracted using natural language processing tools, and apply clustering and predictive techniques to this dataset, to predict potential key actors---individuals who have a central role in overtly criminal activities, or activities which could lead to later offending, and hence might benefit most from interventions. We predict 49 key actors on an underground gaming-specific cheating and hacking forum, validated by observing only overlaps of techniques, combined with topic analysis, to build a classifier for key actor status. In addition, we also use these techniques to provide further insight of key actor activity. We found one cluster and two posting trajectories to contain a high proportion of key actors, logistic regression found an actor's h-index to have higher odds for prediction than other features, and partial dependence plots found reputation to have a significant change in prediction between values of 100 to 1000

The Role of Virtual Communities in Gambling and Gaming Behaviors : A Systematic Review

Gambling opportunities are facilitated by the growth of the Internet and social media platforms. Digital games also increasingly include monetary features, such as microtransactions, blurring the line between gambling and gaming. The Internet provides a variety of virtual communities for gamblers and gamers, but comprehensive research on these communities and their relevance in gambling and monetary gaming behaviors remains scarce. This paper summarizes research of online gambling and monetary gaming communities based on a systematic literature review. A systematic literature search was conducted from five databases: Scopus, Web of Science, PsycINFO, Social Science Premium Collection, and EBSCOhost. The search was limited to empirical articles that focused on gambling or gaming involving money and examined online interaction between gamblers or gamers. Preliminary search resulted in 1056 articles, from which 55 were selected for the analyses based on pre-determined criteria. According to results, online communities serve different functions in gambling and gaming behaviors. Gambling communities are typically forums for discussing and sharing gambling experiences, strategies, and tips as well as gambling problems, while gaming communities are inherently embedded inside a game being an essential part of the gaming experience. Identification with virtual communities influences gambling behavior and monetary gaming behavior through mechanisms of perceived norms, social influence, and community feedback. Whereas some gambling communities may provide protection from excessive gambling habits, gaming communities seem to solely motivate gaming behavior and purchase intentions. The role of online communities should be acknowledged in prevention and treatment of gambling and gaming problems.Peer reviewe

Essays on the New Blockchain-Based Digital Financial Market : Risks and Opportunities

This doctoral thesis consists of five original essays on the risks and opportunities of the new blockchain-based digital financial market. The purpose of this dissertation is to analyze, identify, and, if possible, predict some of the major risks in the market for blockchain-based digital assets. It analyzes how crypto-specific characteristics are associated with solvency risk, sustainability risk, seclusion risk, and sentiment risk. On top of that, it also sheds light on the opportunity side of this financial innovation. The first essay of this dissertation specifically focuses on cryptocurrency for solvency risks. To forecast potential cryptocurrency default at an early stage, this study focuses on variables that are part of the information set of the investor 1 month at most after the start of trading for a cryptocurrency. The results of this research show that bankruptcies among cryptocurrencies are predictable. The second essay explores energy risk as a fundamental market-driving force for the pricing of cryptocurrency. Cryptocurrencies using a high-energy-consumption consensus protocol are riskier than others because their mining costs are more exposed to changes in energy price. Surprisingly, the study finds that energy consumption does not seem to play a role in pricing cryptocurrency. The third essay hypothesizes that privacy coins form a distinct submarket in the cryptocurrency market, shedding light on seclusion risk. It shows that privacy coins and non-privacy coins are two distinct asset markets within the cryptocurrency market. The fourth essay is about news media sentiment risk. It explores whether news media sentiments have an impact on Bitcoin volatility. It also differentiates financial sentiment and psychological sentiment and finds that financially optimistic investors are driving the Bitcoin market. On the other hand, the fifth essay in this dissertation analyzes opportunities, especially the funding opportunity in the widely known category of new digital assets defined as crypto tokens. It analyzes the determinants of the success of initial coin offerings and finds that initial-coin-offering investors are largely guided by their emotions when making investment decisions. Surprisingly, regulatory framework has not yet become a priority among policymakers. Therefore, this doctoral dissertation not only facilitates future research, but also helps regulators in shaping the future of blockchain-based financial technologies.Tämä väitöskirja koostuu viidestä esseestä, jotka käsittelevät uuden lohkoketjupohjaisen digitaalisen rahoitusmarkkinan riskejä ja mahdollisuuksia. Väitöskirjan tarkoituksena on analysoida, tunnistaa ja mahdollisuuksien mukaan ennustaa joitakin lohkoketjupohjaisten digitaalisten varojen markkinoiden suurimpia riskejä. Siinä analysoidaan, miten kryptovaluuttakohtaiset ominaisuudet liittyvät vakavaraisuusriskiin, kestävyysriskiin, eristäytymisriskiin ja sentimenttiriskiin. Tämän lisäksi se valottaa myös tämän rahoitusinnovaation mahdollisuuksia. Tämän väitöskirjan ensimmäisessä esseessä keskitytään erityisesti kryptovaluuttaan maksukyvyttömyysriskinä. Tässä tutkimuksessa keskitytään muuttujiin, jotka ovat sijoittajan saatavilla korkeintaan 1 kuukausi sen jälkeen, kun kaupankäynti kryptovaluutalla on alkanut. Tämän tutkimuksen tulokset osoittavat, että kryptovaluuttojen konkurssit ovat ennustettavissa. Toisessa esseessä tutkitaan energiariskiä markkinoita ohjaavana voimana kryptovaluutan hinnoittelussa. Kryptovaluutat, jotka käyttävät paljon energiaa kuluttavaa konsensusprotokollaa, ovat muita riskialttiimpia, koska niiden louhintakustannukset ovat alttiimpia energian hinnan muutoksille. Yllättäen tutkimuksessa todetaan, että energiankulutuksella ei näytä olevan merkitystä kryptovaluuttojen hinnoittelussa. Kolmannessa esseessä hypoteesina on, että yksityisyyskolikot muodostavat erillisen alamarkkinan kryptovaluuttamarkkinoilla, ja tutkimus tarkastelee näiden eristäytymisriskiä. Siinä osoitetaan, että yksityisyyskolikot ja ei-yksityisyyskolikot ovat kaksi erillistä omaisuuserämarkkinaa kryptovaluuttamarkkinoilla. Neljäs essee käsittelee uutismedian sentimenttiriskiä. Siinä tutkitaan, vaikuttaako uutismedian sentimentti Bitcoinin volatiliteettiin. Siinä myös erotetaan toisistaan taloudellinen sentimentti ja psykologinen sentimentti ja todetaan, että taloudellisesti optimistiset sijoittajat ohjaavat Bitcoin-markkinoita. Väitöskirjan viidennessä esseessä analysoidaan mahdollisuuksia, erityisesti rahoitusmahdollisuuksi, liittyen laajalti tunnettuihin digitaalisiin tokeneihin. Siinä havaitaan, että näihin omaisuuseriin sijoittavat sijoittajat toimivat pitkälti tunteidensa ohjaamina sijoituspäätöksiä tehdessään. Yllättävää kyllä, sääntelykehyksestä ei ole vielä tullut poliittisten päättäjien prioriteettia. Siksi tämä väitöskirja ei ainoastaan tue tulevaa tutkimusta, vaan auttaa myös viranomaisia lohkoketjupohjaisten rahoitusteknologioiden tulevaisuuden määrittelyssä.fi=vertaisarvioitu|en=peerReviewed

Sentiment analysis: the case of twitch chat - Mining user feedback from livestream chats

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementIn a world where users often share their thoughts and opinions through online communication channels, applications that can tap into these channels as to extract consumer feedback have become increasingly valuable. Traditional marketing research techniques such as interviews or surveys offer results that pale in comparison to sentiment analysis applications that can extract organic feedback from an extremely large selection, with very little resources and in real-time. This thesis focuses on proposing and developing one of these tools that targets livestreams, which have, over the years, seen a massive increase in popularity from both a user-base standpoint as well as brand involvement. We chose the livestreaming platform “Twitch” as the target of research and developed a sentiment analysis model, using rule-based approaches, capable of interpreting user chat messages and identifying whether those messages are negative, positive or neutral. Additionally, an application was developed to better view and analyze the results of the model. By segmenting our results by product reveal, we also exhibit how the application allows for the extraction of various insights about the public’s opinion of that product

Evaluating key strategic factors impacting the socio economic growth and sustainability of race horse owners in KZN.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.The prestige of horse racing is enjoyed by many people in most countries in the world and the sport has historically become known as the “Sport of Kings”. In South Africa (SA) and in many other countries, the sport of horse racing has faced unparalleled competition which has resulted in a rapid decline of industry participants in particular race horse owners. Race horse owners are one of the pivotal players in the sport especially because they are a key source of funding for the horse racing industry. A decline in ownership leads to a decline in betting product which results in overall decline in revenue generated from gambling leading to a further decline in ownership. This perpetuating cycle could lead to the industry becoming unsustainable. This study aims to identify the key strategic factors impacting the socio-economic growth and sustainability of race horse owners in KZN. This statement is in itself paradoxical as the study presents evidence of a “decline” of horse ownership in KZN. In order to achieve these objectives, quantitative research methods were used to collect data. Primary data was sourced through quantitative analysis using a self-administered questionnaire distributed to horse owners in KZN using the QuestionPro survey software. Stratified random sampling was used to select the respondents to the self-administered questionnaire. The population comprised 1120 past and present owners. The sample size amounted to 267 participants of which 106 responded. Descriptive statistics were used to analyse the quantitative data. Secondary data was also obtained from company records, industry analysis and government publications. The main findings of the study indicate that prize money (stakes) paid to race horse owners when their horse wins or places in a race, is too low. One of the big constraints faced by owners is that stakes are not increasing in line with costs. In 2011, prize money covered only 46% of the cost of race horse ownership in SA (International Federation of Horse Racing, 2014). The horse racing industry in SA is a labour intensive industry, and this negative economic trend is unfortunate especially from a socio-economic perspective in a country with a high level of unemployment. The motivation behind the study is to identify ways of overcoming or mitigating the challenges that exist so as to positively impact the industry and the regional economy at large. Recommendations to achieve this include the promotion of syndicates as a form of ownership and a marketing strategy that focuses on innovation and technology

Modeling Human Group Behavior In Virtual Worlds

Virtual worlds and massively-multiplayer online games are rich sources of information about large-scale teams and groups, offering the tantalizing possibility of harvesting data about group formation, social networks, and network evolution. They provide new outlets for human social interaction that differ from both face-to-face interactions and non-physically-embodied social networking tools such as Facebook and Twitter. We aim to study group dynamics in these virtual worlds by collecting and analyzing public conversational patterns of users grouped in close physical proximity. To do this, we created a set of tools for monitoring, partitioning, and analyzing unstructured conversations between changing groups of participants in Second Life, a massively multi-player online user-constructed environment that allows users to construct and inhabit their own 3D world. Although there are some cues in the dialog, determining social interactions from unstructured chat data alone is a difficult problem, since these environments lack many of the cues that facilitate natural language processing in other conversational settings and different types of social media. Public chat data often features players who speak simultaneously, use jargon and emoticons, and only erratically adhere to conversational norms. Humans are adept social animals capable of identifying friendship groups from a combination of linguistic cues and social network patterns. But what is more important, the content of what people say or their history of social interactions? Moreover, is it possible to identify whether iii people are part of a group with changing membership merely from general network properties, such as measures of centrality and latent communities? These are the questions that we aim to answer in this thesis. The contributions of this thesis include: 1) a link prediction algorithm for identifying friendship relationships from unstructured chat data 2) a method for identifying social groups based on the results of community detection and topic analysis. The output of these two algorithms (links and group membership) are useful for studying a variety of research questions about human behavior in virtual worlds. To demonstrate this we have performed a longitudinal analysis of human groups in different regions of the Second Life virtual world. We believe that studies performed with our tools in virtual worlds will be a useful stepping stone toward creating a rich computational model of human group dynamics

Fear sells: On the sentiment deceptions and fundraising success of initial coin offerings

Retrieving information from an intensive hand-collected whitepaper data library covering 5,033 ICOs launched before 2020, we analyze the determinants of ICO success as measured by the amount of raised funding. We assess the sentiment and readability in ICO whitepapers in addition to other information disclosures. Whereas we do not find any evidence for that the riskiness of ICO projects would lower the predicted amount of raised funding, our results strongly suggest that ICO investors are largely guided by emotions when making investment decisions. Contrary to earlier literature, we find a weak association between quality signals of whitepapers and its success.© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Located Lexicon: a project that explores how user generated content describes place

This extended conference paper explores the use and potential of location data in social media contexts. The research involved a series of experiments undertaken to assess the extent to which location information is present in exchanges, directly or indirectly. A prototype application was designed to exploit the insight obtained from the data-gathering experiments. This enabled us to develop a method and toolkit for searching, extracting and visualising mass-generated data for open source use. Ultimately, we were able to generate insights into data quality and ‘scale of query’ for emerging pedagogical research in learning swarms and distributed learners