Social Media Fake Account Detection for Afan Oromo Language using Machine Learning

A social networking service serves as a platform to build social networks or social relations among people who, share interests, activities, backgrounds, or real life connections. A social network service is generally offered to participants who registers to this site with their unique representation (often a profile) and one’s social links. Most social network services are web-based and provide means for users to interact over the Internet. (M. Smruthi, , February 2019).Online social networking sites became an important means in our daily life. Millions of users register and share personal information with others. Because of the fast expansion of social networks, public may exploit them for unprincipled and illegitimate activities. As a result of this, privacy threats and disclosing personal information have become the most important issues to the users of social networking sites. The intent of creating fake profiles have become an adversary effect and difficult to detect such identities/malicious content without appropriate research. The current research that have been developed for detecting malicious content, primarily considered the characteristics of user profile. Most of the existing techniques lack comprehensive evaluation. In this work we propose new model using machine learning and NLP (Natural Language Processing) techniques to enhance the accuracy rate in detecting the fake identities in online social networks. We would like to apply this approach to Facebook by extracting the features like Time, date of publication, language, and geo position. (Srinivas Rao Pulluri1, A Comprehensive Model for Detecting Fake Profiles in Online Social Networks, 2017) DOI: 10.7176/NMMC/90-01 Publication date:May 31st 2020

Fake Account Identification Using Machine Learning Approaches Integrated with Adaptive Particle Swarm Optimization

 It is customary for humans, bots, and other automated systems to generate new user accounts by utilizing pilfered or otherwise deceitful personal information. They are employed in deceitful activities such as phishing and identity theft, as well as in spreading damaging rumors. An somebody with malevolent intent may generate a substantial number of counterfeit accounts, ranging from hundreds to thousands, with the aim of disseminating their harmful actions to as many authentic users as possible. Users can get a wealth of knowledge from social networking networks. Malicious individuals are readily encouraged to take use of this vast collection of social media information. These cybercriminals fabricate fictitious identities and disseminate meaningless stuff. An essential aspect of using social media networks is the process of discerning counterfeit profiles. This study presents a machine learning approach to detect fraudulent Instagram profiles. This strategy employed the attribute-selection technique, adaptive particle swarm optimization, and feature-elimination recursion. The results indicate that the suggested adaptive particle swarm optimization method surpasses RFE in terms of accuracy, recall, and F measure

Classification of instagram fake users using supervised machine learning algorithms

On Instagram, the number of followers is a common success indicator. Hence, followers selling services become a huge part of the market. Influencers become bombarded with fake followers and this causes a business owner to pay more than they should for a brand endorsement. Identifying fake followers becomes important to determine the authenticity of an influencer. This research aims to identify fake users' behavior, and proposes supervised machine learning models to classify authentic and fake users. The dataset contains fake users bought from various sources, and authentic users. There are 17 features used, based on these sources: 6 metadata, 3 media info, 2 engagement, 2 media tags, 4 media similarity. Five machine learning algorithms will be tested. Three different approaches of classification are proposed, i.e. classification to 2-classes and 4-classes, and classification with metadata. Random forest algorithm produces the highest accuracy for the 2-classes (authentic, fake) and 4-classes (authentic, active fake user, inactive fake user, spammer) classification, with accuracy up to 91.76%. The result also shows that the five metadata variables, i.e. number of posts, followers, biography length, following, and link availability are the biggest predictors for the users class. Additionally, descriptive statistics results reveal noticeable differences between fake and authentic users

Fake accounts detection system based on bidirectional gated recurrent unit neural network

Online social networks have become the most widely used medium to interact with friends and family, share news and important events or publish daily activities. However, this growing popularity has made social networks a target for suspicious exploitation such as the spreading of misleading or malicious information, making them less reliable and less trustworthy. In this paper, a fake account detection system based on the bidirectional gated recurrent unit (BiGRU) model is proposed. The focus has been on the content of users’ tweets to classify twitter user profile as legitimate or fake. Tweets are gathered in a single file and are transformed into a vector space using the GloVe word embedding technique in order to preserve the semantic and syntax context. Compared with the baseline models such as long short-term memory (LSTM) and convolutional neural networks (CNN), the results are promising and confirm that using GloVe with BiGRU classifier outperforms with 99.44% for accuracy and 99.25% for precision. To prove the efficiency of our approach the results obtained with GloVe were compared to Word2vec under the same conditions. Results confirm that GloVe with BiGRU classifier performs the best results for detection of fake Twitter accounts using only tweets content feature

Fake News Detection on Twitter Using Propagation Structures

The growth of social media has revolutionized the way people access information. Although platforms like Facebook and Twitter allow for a quicker, wider and less restricted access to information, they also consist of a breeding ground for the dissemination of fake news. Most of the existing literature on fake news detection on social media proposes user-based or content-based approaches. However, recent research revealed that real and fake news also propagate significantly differently on Twitter. Nonetheless, only a few articles so far have explored the use of propagation features in their detection. Additionally, most of them have based their analysis on a narrow tweet retrieval methodology that only considers tweets to be propagating a news piece if they explicitly contain an URL link to an online news article. By basing our analysis on a broader tweet retrieval methodology that also allows tweets without an URL link to be considered as propagating a news piece, we contribute to fill this research gap and further confirm the potential of using propagation features to detect fake news on Twitter. We firstly show that real news are significantly bigger in size, are spread by users with more followers and less followings, and are actively spread on Twitter for a longer period of time than fake news. Secondly, we achieve an 87% accuracy using a Random Forest Classifier solely trained on propagation features. Lastly, we design a Geometric Deep Learning approach to the problem by building a graph neural network that directly learns on the propagation graphs and achieve an accuracy of 73.3%

An Enhanced Scammer Detection Model for Online Social Network Frauds Using Machine Learning

The prevalence of online social networking increase in the risk of social network scams or fraud. Scammers often create fake profiles to trick unsuspecting users into fraudulent activities. Therefore, it is important to be able to identify these scammer profiles and prevent fraud such as dating scams, compromised accounts, and fake profiles. This study proposes an enhanced scammer detection model that utilizes user profile attributes and images to identify scammer profiles in online social networks. The approach involves preprocessing user profile data, extracting features, and machine learning algorithms for classification. The system was tested on a dataset created specifically for this study and was found to have an accuracy rate of 94.50% with low false-positive rates. The proposed approach aims to detect scammer profiles early on to prevent online social network fraud and ensure a safer environment for society and women’s safety

O impacto da inteligência artificial no negócio eletrónico

Pela importância que a Inteligência Artificial exibe na atualidade, revela-se de grande interesse verificar até que ponto ela está a transformar o Negócio Eletrónico. Para esse efeito, delineou-se uma revisão sistemática com o objetivo de avaliar os impactos da proliferação destes instrumentos. A investigação empreendida pretendeu identificar artigos científicos que, através de pesquisas realizadas a Fontes de Dados Eletrónicas, pudessem responder às questões de investigação implementadas: a) que tipo de soluções, baseadas na Inteligência Artificial (IA), têm sido usadas para melhorar o Negócio Eletrónico (NE); b) em que domínios do NE a IA foi aplicada; c) qual a taxa de sucesso ou fracasso do projeto. Simultaneamente, tiveram de respeitar critérios de seleção, nomeadamente, estar escritos em inglês, encontrarem-se no intervalo temporal 2015/2021 e tratar-se de estudos empíricos, suportados em dados reais. Após uma avaliação de qualidade final, procedeu-se à extração dos dados pertinentes para a investigação, para formulários criados em MS Excel. Estes dados estiveram na base da análise quantitativa e qualitativa que evidenciaram as descobertas feitas e sobre os quais se procedeu, posteriormente, à sua discussão. A dissertação termina com as conclusão e discussão de trabalhos futuros.Due to the importance that Artificial Intelligence exhibits today, it is of great interest to see to what extent it is transforming the Electronic Business. To this end, a systematic review was designed to evaluate the impacts of the proliferation of these instruments. The research aimed to identify scientific articles that, through research carried out on Electronic Data Sources, could answer the research questions implemented: a) what kind of solutions, based on Artificial Intelligence, have been used to improve the Electronic Business; b) in which areas of the Electronic Business Artificial Intelligence has been applied; c) what the success rate or failure of the project is. At the same time, they must comply with selection criteria, to be written in English, to be found in the 2015/2021-time interval and to be empirical studies supported by actual data. After a final quality evaluation, the relevant data for the investigation were extracted for forms created in MS Excel. These data were the basis of the quantitative and qualitative analysis that evidenced the findings found and on which they were subsequently discussed. The dissertation ends with the conclusion and discussion of future works

Player valuation in thin markets: the case of European Association football

The amount of money in football is staggering and is a concern for people of all walks of life. While these concerns are valid, the money in football is justified and consumers of football as a form of entertainment, actively participate in the set-up of this labour market. Thanks to the availability of market value, wage, and transfer fee data for the most valued production workers (players) and bolstered by the emergence of data analytics firms to crunch large amounts of performance data in real time, it is possible to analyse and better understand the monetary worth of the most talented players, and the role of each stakeholder in the buildup of this value. This 3-essay series uses Mincer’s (1985) human capital formulation and multilevel regression analyses to provide a complete study of the different money centers that underlie player valuation.Essay 1 analyses player market values – values attributed by football fans via crowd-sourced open forums online. Market values (Transfermarkt values) that are used in actual transfer and salary negotiations are driven by both football and non-football related factors. From a sample of 500 offensive player observations in the big 5 European leagues for the 2017/18 and 2018/19 seasons, this essay analyses 12 data points per player observation, hence 6,000 data points in total, using a series of multilevel regression models to isolate the proportion of player market value based solely on talent (performance and demographic). Results show that the proportion of market value due to talent decreases as market value increases. For the players sampled, the mean impact of talent on overall market value is 77%. Essay 2 analyses the transfer fee premia. The difference between the amount paid for the transfer of a football player and his crowd-sourced market valuation at the time of transfer (transfer premium) is dependent on several factors some of which are not measurable. This essay analyses 30 top transfers per season over the decade 2011 – 2020 and shows that buying clubs exhibit risk tolerance in that they spend a sizeable premium on young promising players compared to mature players with proven talent. The breach of a player’s current contract and player’s overall performance rating during the previous season also play significant roles in the size of the transfer premium.Essay 3 looks at the top end of the football market valuation and shows that there are no diminishing returns on player wages as age increases. An analysis of the 90th percentile of football players in Europe’s ‘big 5’ leagues, ranked by Transfermarkt market value, shows that mature players earn 112% more than young players, while mid age players earn 64% more than young players. Transfers in this market segment come with a wage penalty, but compared to young players, mature players get an offset. Player performance and minutes played in the preceding season do not matter much in wage determination as players in this market segment already have reputation built over the years. Player popularity has a small positive effect on the basic wage of football players compared to the impact on their bonuses and image rights.The player labour markets shows that clubs exhibit risk tolerance in player transfers by their willingness to spend huge amounts on the transfer of young players with no proven talent in the hopes that this investment will pay-off in the future. On the other hand, when it comes to wages, clubs exhibit risk aversion as they pay much higher wages to mature players with proven talent

Dismiss : uma abordagem para análise sociotécnica da desinformação digital

Orientador: Dr. Roberto PereiraTese (doutorado) - Universidade Federal do Paraná, Setor de Ciencias Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 28/08/2023Inclui referênciasÁrea de concentração: Ciência da ComputaçãoResumo: Essa tese aborda o desafio de entender e lidar com a desinformação digital como um fenômeno sociotécnico, ou seja, que envolve tanto aspectos das tecnologias utilizadas para comunicação quanto do contexto humano/social em que a desinformação ocorre. Os resultados de nosso mapeamento sistemático da literatura mostraram que projetistas de intervenções para mitigação da desinformação têm dificuldades em lidar com a natureza sociotécnica do fenômeno, tendem a utilizar abordagens disciplinares focadas nos aspectos técnicos da desinformação e abordam o fenômeno de forma segmentada. Essas dificuldades podem levar os projetistas à ignorarem aspectos relevantes para o entendimento do fenômeno e à soluções com potenciais prejudiciais, como a censura ou avisos invasivos. Nesse sentido, essa tese investiga meios para apoiar projetistas a compreenderem o fenômeno pela perspectiva sociotécnica, ajudando a caracterizar casos de desinformação digital e auxiliando no entendimento abrangente de problemas. Como solução, essa tese apresenta a Dismiss - uma aborDagem para análIse Sociotécnica de Deinformações DigItaiS. A Dismiss é fundamentada na Semiótica Organizacional, composta pelo Modelo Conceitual do Ciclo de Vida da Desinformação Digital, artefatos e materiais de apoio que amparam a análise sociotécnica da desinformação. A abordagem representa uma ferramenta epistêmica projetada para proporcionar a reflexão de seus utilizadores sobre as circunstâncias em que a desinformação ocorre, auxiliando na compreensão da origem e consequências da desinformação digital. A Dismiss foi avaliada de forma construtiva ao longo de seu desenvolvimento, usando métodos de grupo focal (11 encontros), estudos em pequena escala (7 casos), e oficinas de análise sociotécnica de casos de desinformação digital com representantes do público-alvo (3 oficinas). Os resultados dos grupos focais e estudos em pequena escala informaram o refinamento da abordagem, sua estrutura, componentes e métodos de aplicação. Os resultados das oficinas indicam a utilidade percebida da abordagem em apoiar a compreensão da desinformação como um fenômeno sociotécnico. Os resultados também indicaram aspectos que podem ser aprimorados na Dismiss, como a quantidade de passos, a explicação de artefatos, e a densidade dos materiais de apoio, informando melhoriasAbstract: This thesis addresses the challenge of understanding and dealing with digital misinformation as a sociotechnical phenomenon, meaning that it involves both aspects of the technologies used for communication and the human/social context in which misinformation occurs. The results of our systematic literature review showed that designers of interventions for mitigating misinformation face difficulties in dealing with the sociotechnical nature of the phenomenon. They tend to employ disciplinary approaches focused on the technical aspects of misinformation and often address the phenomenon in a fragmented manner. These difficulties can lead designers to overlook relevant aspects for understanding the phenomenon and result in potentially harmful solutions, such as censorship or invasive warnings. In this regard, this thesis investigates means to support designers in comprehending the phenomenon from a sociotechnical perspective, helping to characterize cases of digital misinformation and aiding in a comprehensive understanding of the issues. As a solution, this thesis presents Dismiss - an Approach for Sociotechnical Analysis of Digital Misinformation. Dismiss is grounded in Organizational Semiotics, comprised of the Conceptual Model of the Digital Misinformation Lifecycle, artifacts, and supporting materials that underpin the sociotechnical analysis of misinformation. The approach serves as an epistemic tool designed to facilitate users’ reflection on the circumstances in which misinformation occurs, assisting in understanding the origins and consequences of digital misinformation. Dismiss was constructively evaluated throughout its development, utilizing focus group methods (11 meetings), small-scale studies (7 cases), and workshops for the sociotechnical analysis of digital misinformation cases with representatives of the target audience (3 workshops). The results from the focus groups and small-scale studies informed the refinement of the approach, its structure, components, and application methods. The workshop results indicate the perceived utility of the approach in supporting the understanding of misinformation as a sociotechnical phenomenon. The results also highlighted aspects that can be improved in Dismiss, such as the number of steps, artifact explanations, and the density of supporting materials, providing insights for enhancement