Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on Twitter

Microblogs are increasingly exploited for predicting prices and traded volumes of stocks in financial markets. However, it has been demonstrated that much of the content shared in microblogging platforms is created and publicized by bots and spammers. Yet, the presence (or lack thereof) and the impact of fake stock microblogs has never systematically been investigated before. Here, we study 9M tweets related to stocks of the 5 main financial markets in the US. By comparing tweets with financial data from Google Finance, we highlight important characteristics of Twitter stock microblogs. More importantly, we uncover a malicious practice - referred to as cashtag piggybacking - perpetrated by coordinated groups of bots and likely aimed at promoting low-value stocks by exploiting the popularity of high-value ones. Among the findings of our study is that as much as 71% of the authors of suspicious financial tweets are classified as bots by a state-of-the-art spambot detection algorithm. Furthermore, 37% of them were suspended by Twitter a few months after our investigation. Our results call for the adoption of spam and bot detection techniques in all studies and applications that exploit user-generated content for predicting the stock market

Fame for sale: efficient detection of fake Twitter followers

$\textit{Fake followers}$ are those Twitter accounts specifically created to inflate the number of followers of a target account. Fake followers are dangerous for the social platform and beyond, since they may alter concepts like popularity and influence in the Twittersphere - hence impacting on economy, politics, and society. In this paper, we contribute along different dimensions. First, we review some of the most relevant existing features and rules (proposed by Academia and Media) for anomalous Twitter accounts detection. Second, we create a baseline dataset of verified human and fake follower accounts. Such baseline dataset is publicly available to the scientific community. Then, we exploit the baseline dataset to train a set of machine-learning classifiers built over the reviewed rules and features. Our results show that most of the rules proposed by Media provide unsatisfactory performance in revealing fake followers, while features proposed in the past by Academia for spam detection provide good results. Building on the most promising features, we revise the classifiers both in terms of reduction of overfitting and cost for gathering the data needed to compute the features. The final result is a novel $\textit{Class A}$ classifier, general enough to thwart overfitting, lightweight thanks to the usage of the less costly features, and still able to correctly classify more than 95% of the accounts of the original training set. We ultimately perform an information fusion-based sensitivity analysis, to assess the global sensitivity of each of the features employed by the classifier. The findings reported in this paper, other than being supported by a thorough experimental methodology and interesting on their own, also pave the way for further investigation on the novel issue of fake Twitter followers

Susceptibility to Social Engineering in Social Networking Sites: The Case of Facebook

Past research has suggested that social engineering poses the most significant security risk. Recent studies have suggested that social networking sites (SNSs) are the most common source of social engineering attacks. The risk of social engineering attacks in SNSs is associated with the difficulty of making accurate judgments regarding source credibility in the virtual environment of SNSs. In this paper, we quantitatively investigate source credibility dimensions in terms of social engineering on Facebook, as well as the source characteristics that influence Facebook users to judge an attacker as credible, therefore making them susceptible to victimization. Moreover, in order to predict users’ susceptibility to social engineering victimization based on their demographics, we investigate the effectiveness of source characteristics on different demographic groups by measuring the consent intentions and behavior responses of users to social engineering requests using a role-play experiment

Predictive Analysis on Twitter: Techniques and Applications

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories

A trend study on the impact of social media on advertisement

This paper presents a comprehensive scientometric study for the impact of social networks on advertisement. The study uses the Scopus database as a search engine to accomplish the survey. To better understand the evolution and identity of this category, the study covers 1216 most cited data over the period 1983-2019. Qualitative and quantitative data analysis techniques are applied to determine author distribution, country, individual and institutional-level productivity rankings. In terms of keywords, the study indicates that social media was jointly studied with gender and be-havior and researchers from the United States maintained the highest rate of contribution. The survey also indicates that there were strong collaboration between the researchers from China and United States. Moreover, there were also remarkable collaborations between the researchers in United States from one side and other countries

A Survey of Social Network Forensics

Social networks in any form, specifically online social networks (OSNs), are becoming a part of our everyday life in this new millennium especially with the advanced and simple communication technologies through easily accessible devices such as smartphones and tablets. The data generated through the use of these technologies need to be analyzed for forensic purposes when criminal and terrorist activities are involved. In order to deal with the forensic implications of social networks, current research on both digital forensics and social networks need to be incorporated and understood. This will help digital forensics investigators to predict, detect and even prevent any criminal activities in different forms. It will also help researchers to develop new models / techniques in the future. This paper provides literature review of the social network forensics methods, models, and techniques in order to provide an overview to the researchers for their future works as well as the law enforcement investigators for their investigations when crimes are committed in the cyber space. It also provides awareness and defense methods for OSN users in order to protect them against to social attacks

De promocionar a vender moda: evolución de perfil público en redes sociales a e-commerce de éxito internacional

[Resumen] En el actual contexto de transformación provocado por el auge de la digitalización y las redes sociales, que han propiciado a su vez un cambio en los hábitos de consumo y las relaciones personales, este trabajo tiene como objetivo principal identificar los distintos factores que posibilitan la transición de perfiles públicos en redes sociales a plataformas de e-commerce de éxito internacional, definiendo un nuevo modelo de negocio, en este caso centrado en el sector de la moda. Otro de los objetivos de este estudio es que permita extraer conclusiones extrapolables a otras marcas de similar formato. Tras el desarrollo inicial de un marco teórico-conceptual para poder situar y enfocar al lector en el tema tratado, se sigue una metodología mixta, con un enfoque cuantitativo y cualitativo. Por un lado, se ha elaborado un cuestionario para determinar los rasgos y la motivación de compra en estos modelos de negocio, del que se han obtenido un total de 907 respuestas de consumidores. Por otro lado, se desarrolla un estudio de caso de la marca Laagam; marca que se inició a partir del perfil social público de Inés Arroyo y que actualmente genera ventas a nivel internacional a través de diversos canales online. Para obtener información para este caso, se ha realizado una entrevista semiestructurada a la directora de contenidos de dicha firma. El objetivo del caso de estudio es identificar, en un negocio internacional de moda en funcionamiento, aquellos factores y conclusiones que previamente se han desarrollado en un marco teórico y estudiar además la presencia de otros posibles factores que puedan servir al objetivo final del estudio. El análisis de los datos obtenidos permite detectar los cambios en el comportamiento del consumidor, identificando distintos tipos de actuaciones, poniendo el foco en un consumidor social, joven, interconectado y con un alto nivel de planificación, que dispone cada vez de más canales online a su disposición, que cada vez está más influenciado por las redes sociales y otros consumidores, y que busca constantemente información y opiniones de usuarios en redes antes de realizar una compra. Su principal motivación para efectuar dicha compra es la búsqueda de valor a través de la diferenciación y la vivencia de experiencias durante todo el proceso. Se identifican, además, aquellos factores que conducen al éxito de este tipo de marcas, que como Laagam, provienen de un perfil público en redes y en las que resulta determinante la existencia de una estrategia omnicanal adecuada. Finalmente, tras las conclusiones, se mencionan las principales limitaciones de este trabajo.[Abstract] In the current context of transformation caused by the rise of digitalisation and social networks, which have in turn led to a change in consumer habits and personal relationships, the main objective of this study is to identify the different factors that make possible the transition from public profiles on social networks to internationally successful e-commerce platforms, defining a new business model, in this case focused on the fashion sector. Another of the objectives of this study is to draw conclusions that can be extrapolated to other brands of a similar format. After the initial development of a theoretical-conceptual framework in order to situate and focus the reader on the subject, a mixed methodology is followed, with a quantitative and qualitative approach. On the one hand, a questionnaire has been developed to determine the traits and purchase motivation in these business models, from which a total of 907 consumer responses have been obtained. On the other hand, a case study of the Laagam brand was developed; a brand that started from the public social profile of Ines Arroyo and currently generates sales internationally through various online channels. To obtain information for this case, a semi-structured interview was conducted with the content director of the firm. The objective of the case study is to identify, in an international fashion business in operation, those factors and conclusions that have been previously developed in a theoretical framework and also to study the presence of other possible factors that may serve the final objective of the study. The analysis of the data obtained allows us to detect changes in consumer behaviour, identifying different types of actions, focusing on a social, young, interconnected consumer with a high level of planning, who has more and more online channels at his disposal, who is increasingly influenced by social networks and other consumers, and who constantly seeks information and opinions from users on networks before making a purchase. Their main motivation for making a purchase is the search for value through differentiation and experience throughout the process. It also identifies the factors that lead to the success of this type of brands, which, like Laagam, come from a public profile in networks and in which the existence of an appropriate omnichannel strategy is a determining factor. Finally, after the conclusions, the main limitations of this work are mentioned.[Resumo] No actual contexto de transformación provocado polo auxe da dixitalización e as redes sociais, que propiciaron á súa vez un cambio nos hábitos de consumo e as relacións persoais, este traballo ten como obxectivo principal identificar os distintos factores que posibilitan a transición de perfís públicos en redes sociais a plataformas de e-commerce de éxito internacional, definindo un novo modelo de negocio, neste caso centrado no sector da moda. Outro dos obxectivos deste estudo é que permita extraer conclusións extrapolables a outras marcas de similar formato. Tras o desenvolvemento inicial dun marco teórico-conceptual para poder situar e enfocar ao lector no tema tratado, séguese unha metodoloxía mixta, cun enfoque cuantitativo e cualitativo. Por unha banda, elaborouse un cuestionario para determinar os trazos e a motivación de compra nestes modelos de negocio, do que se obtiveron un total de 907 respostas de consumidores. Doutra banda, desenvólvese un estudo de caso da marca Laagam; marca que se iniciou a partir do perfil social público de Inés Arroyo e que actualmente xera vendas a nivel internacional a través de diversas canles en liña. Para obter información para este caso, realizouse unha entrevista semiestruturada á directora de contidos da devandita firma. O obxectivo do caso de estudo é identificar, nun negocio internacional de moda en funcionamento, aqueles factores e conclusións que previamente se desenvolveron nun marco teórico e estudar ademais a presenza doutros posibles factores que poidan servir ao obxectivo final do estudo. A análise dos datos obtidos permite detectar os cambios no comportamento do consumidor, identificando distintos tipos de actuacións, poñendo o foco nun consumidor social, novo, interconetado e cun alto nivel de planificación, que dispón cada vez de máis canles en liña á súa disposición, que cada vez está máis influenciado polas redes sociais e outros consumidores, e que busca constantemente información e opinións de usuarios en redes antes de realizar unha compra. A súa principal motivación para efectuar a devandita compra é a procura de valor a través da diferenciación e a vivencia de experiencias durante todo o proceso. Identifícanse, ademais, aqueles factores que conducen ao éxito deste tipo de marcas, que como Laagam, proveñen dun perfil público en redes e nas que resulta determinante a existencia dunha estratexia omnicanle adecuada. Finalmente, tras as conclusións, menciónanse as principais limitacións deste traballoTraballo fin de grao (UDC.FHD). Xestión Industrial da Moda. Curso 2021/202

Information credibility perception on Twitter

Information on Twitter is vast and varied. Readers must make their own judgements to determine the credibility of the great wealth of information presented on Twitter. This research aims to identify the factors that influence readers' judgements of the credibility of information on Twitter, especially news-related information. Both internal (within the Twitter platform) and external factors are studied in this research. User studies are conducted to collect readers' perceptions of the credibility of news-related tweets, Twitter features, and the impact of reader characteristics, such as a reader's demographic attributes, their personality and behaviour. Twitter readers are found to depend solely on surface tweet features in making these judgements such as the author's Twitter ID, pictures, or the number of retweets and likes, rather than the tweet's metadata as recommended in previous studies. In this study, surface features are related to cognitive heuristics. Cognitive heuristics are features that the mind uses as shortcuts for making quick evaluations such as deciding the credibility of tweets. There are three main types of cognitive heuristic features found on Twitter that readers use to determine credibility: endorsement, reputation and confirmation. This study finds that readers do not use only one single feature to make credibility judgements but rather a combination of features. External factors such as a reader's educational background and geolocation also have a significant positive correlation with their perceptions of a tweet's credibility. Readers with tertiary level education, or living in a certain location or environment, such as in a crisis or conflict area, are observed to be more careful in making credibility judgements. Readers who possess conscientiousness and openness to experience personality traits are also seen to be very cautious in their credibility judgements. Another insight provided by this research is the categorisation of readers' behaviours according to credibility perceptions on Twitter. The behavioural categorisations are defined by readers' behavioural reliance on Twitter's surface features when judging the credibility of tweets. The findings can assist social media authors in designing the surface features of their social media content in order to enhance the content's credibility. Furthermore, findings from this research can help in developing effective credibility evaluation systems by considering readers' personal characteristics

Detecting Deception in Online Social Networks

Online Social Networks (OSNs) play a significant role in the daily life of hundreds of millions of people's. However, many user profiles in OSNs contain deceptive information. Existing studies have shown that lying in OSNs is quite widespread, often for protecting a user's privacy. In this dissertation, we propose a novel approach for detecting deceptive profiles in OSNs. Our ultimate goal is to find deceptive information about user gender and location. We specifically define a set of analysis methods for detecting deceptive information about user genders and locations in Twitter. First, we collected a large dataset of Twitter profiles and tweets. Next, we defined methods for gender guessing from Twitter profile colors and names. Our methods are quite scalable because we avoid the analysis of text messages, which typiclly involves high computational complexity. We applied a number of preprocessing methods to raw Twitter data in ways that significantly enhanced the accuracy of our predictions. Subsequently, we applied Bayesian classification and K-means clustering algorithms to Twitter profile characteristics (e.g., profile layout colors, first names, user names) and geolocations to analyze user behavior. We established the overall accuracy of each gender indicator through extensive experimentations with our crawled dataset