30 research outputs found
An analysis of fake social media engagement services
Fake engagement services allow users of online social media and other web platforms to illegitimately increase their online reach and boost their perceived popularity. Driven by socio-economic and even political motivations, the demand for fake engagement services has increased in the last years, which has incentivized the rise of a vast underground market and support infrastructure. Prior research in this area has been limited to the study of the infrastructure used to provide these services (e.g., botnets) and to the development of algorithms to detect and remove fake activity in online targeted platforms. Yet, the platforms in which these services are sold (known as panels) and the underground markets offering these services have not received much research attention. To fill this knowledge gap, this paper studies Social Media Management (SMM) panels, i.e., reselling platforms¿often found in underground forums¿in which a large variety of fake engagement services are offered. By daily crawling 86 representative SMM panels for 4 months, we harvest a dataset with 2.8 M forum entries grouped into 61k different services. This dataset allows us to build a detailed catalog of the services for sale, the platforms they target, and to derive new insights on fake social engagement services and its market. We then perform an economic analysis of fake engagement services and their trading activities by automatically analyzing 7k threads in underground forums. Our analysis reveals a broad range of offered services and levels of customization, where buyers can acquire fake engagement services by selecting features such as the quality of the service, the speed of delivery, the country of origin, and even personal attributes of the fake account (e.g., gender). The price analysis also yields interesting empirical results, showing significant disparities between prices of the same product across different markets. These observations suggest that the market is still undeveloped and sellers do not know the real market value of the services that they offer, leading them to underprice or overprice their services.This work was supported by the EU Horizon 2020 Research and Innovation Program under Grant agreement no. 101021377 (TRUST aWARE ); the Spanish grants ODIO (PID2019-111429RB-C21 and PID2019-111429RB-C22), and the Region of Madrid grant CYNAMON-CM (P2018/TCS-4566), co-financed by European Structural Funds ESF and FEDER
Dissecting AI-Generated Fake Reviews: Detection and Analysis of GPT-Based Restaurant Reviews on Social Media
Recent advances in generative models such as GPT may be used to fabricate indistinguishable fake customer reviews at a much lower cost, posing challenges for social media platforms to detect this kind of content. This study addresses two research questions: (1) the effective detection of AI-generated restaurant reviews generated from high-quality elite authentic reviews, and (2) the comparison of out-of-sample predicted AI-generated reviews and authentic reviews across multiple dimensions of review, user, restaurant, and content characteristics. We fine-tuned a GPT text detector to predict fake reviews, significantly outperforming existing solutions. We applied the model to predict non-elite reviews that already passed the Yelp filtering system, revealing that AI-generated reviews typically score higher ratings, users posting such content have less established Yelp reputations and AI-generated reviews are more comprehensible and less linguistically complex than human-generated reviews. Notably, machine-generated reviews are more prevalent in low-traffic restaurants in terms of customer visits
Are We All in a Truman Show? Spotting Instagram Crowdturfing through Self-Training
Influencer Marketing generated $16 billion in 2022. Usually, the more popular
influencers are paid more for their collaborations. Thus, many services were
created to boost profiles' popularity metrics through bots or fake accounts.
However, real people recently started participating in such boosting activities
using their real accounts for monetary rewards, generating ungenuine content
that is extremely difficult to detect. To date, no works have attempted to
detect this new phenomenon, known as crowdturfing (CT), on Instagram.
In this work, we propose the first Instagram CT engagement detector. Our
algorithm leverages profiles' characteristics through semi-supervised learning
to spot accounts involved in CT activities. Compared to the supervised
approaches used so far to identify fake accounts, semi-supervised models can
exploit huge quantities of unlabeled data to increase performance. We purchased
and studied 1293 CT profiles from 11 providers to build our self-training
classifier, which reached 95\% F1-score. We tested our model in the wild by
detecting and analyzing CT engagement from 20 mega-influencers (i.e., with more
than one million followers), and discovered that more than 20% was artificial.
We analyzed the CT profiles and comments, showing that it is difficult to
detect these activities based solely on their generated content
Creating Chaos Online
Unmasks the disinformation propagated by Russian trolling in public discours
Marketing Intelligence: Boom or Bust of Service Marketing?
Marketing intelligence fosters two major developments within digital service marketing. On the one hand, a boom of services seems to have evolved, accelerated by the opportunities of marketing intelligence. It has contributed to the optimization of customer experiences, e.g., supported by mobile, personalized, and customized marketing services. On the other hand, (digital) self-services are likely to pervert the term “service”. Lifecycle marketing, including annoying marketing communication in real-time, automated price adjustment and programmatic advertising based on artificial intelligence, affects the vision of fully standardized marketing automation. Additionally, there are incentives to pollute the digital information in order to manufacture opinions. Fake news is one popular example. This leads to the (open) question if marketing intelligence means service boom or bust of marketing. This contribution aims to elaborate the boom-and-bust aspects of marketing intelligence and suggests a trade-off. The method applied in this paper will be a descriptive and conceptual literature review, through which the paradigmatic thoughts will be juxtaposed from the perspective of service
Cultural violence and fragmentation on social media
As a prime example of socio-technological transformation, social media services exert an enormous impact on modern culture. They are nowadays widely established for everyday life uses, but also during natural and man-made crises and political conflicts. For instance, Facebook was part of the Arabic Spring, facilitating the communication and interaction between participants of political protests. However, social media is not only used for good: Based on the notions of cultural violence and cultural peace, this chapter shows the potential for political fragmentation through social media, focusing on fake news and terrorism propaganda and their amplified dissemination through social bots. The chapter shows that regarding both the problematic aspects and the countermeasures, technology plays the role of an amplifier, enabling effects such as astroturfing and smoke screening, but also enhancing social bot detection. This chapter examines what these socio-technological transformations through social media imply in terms of legitimacy and trust
Cyber Security Politics
This book examines new and challenging political aspects of cyber security and presents it as an issue defined by socio-technological uncertainty and political fragmentation. Structured along two broad themes and providing empirical examples for how socio-technical changes and political responses interact, the first part of the book looks at the current use of cyber space in conflictual settings, while the second focuses on political responses by state and non-state actors in an environment defined by uncertainties. Within this, it highlights four key debates that encapsulate the complexities and paradoxes of cyber security politics from a Western perspective – how much political influence states can achieve via cyber operations and what context factors condition the (limited) strategic utility of such operations; the role of emerging digital technologies and how the dynamics of the tech innovation process reinforce the fragmentation of the governance space; how states attempt to uphold stability in cyberspace and, more generally, in their strategic relations; and how the shared responsibility of state, economy, and society for cyber security continues to be re-negotiated in an increasingly trans-sectoral and transnational governance space. This book will be of much interest to students of cyber security, global governance, technology studies, and international relations
O impacto dos influenciadores digitais na escolha de destinos turísticos através do Instagram
Mestrado Bolonha em MarketingEstamos num mundo em que as redes sociais são cada vez mais utilizadas, tanto por
pessoas ou empresas que o inserem nas suas estratégias, considerando que o marketing
de influência é um termo atual e com grandes oportunidades. Posto isto, os
influenciadores digitais têm bastante influência no processo de intenção de compra.
Portanto, este estudo procura obter conhecimentos mais profundos sobre o papel que os
influenciadores digitais desempenham na tomada de decisão de um consumidor,
especificamente quando está a ponderar fazer uma viagem, analisando as características
dos influenciadores que são mais valorizadas pelos seus seguidores, e de que forma a
atitude do consumidor perante os destinos turísticos publicitados é afetada.
A investigação é de natureza exploratória, apresentando uma abordagem qualitativa
com a condução de entrevistas de profundidade, e também explanatória, compondo uma
abordagem quantitativa através de um inquérito por questionário online. Foram feitas
duas entrevistas de profundidade a influenciadoras digitais na área das viagens, baseadas
no Instagram. Do questionário divulgado online, resultaram 104 respostas válidas,
constituindo uma amostra não probabilística e por conveniência.
Após a análise dos resultados, estes evidenciam que algumas características dos
influenciadores digitais como a credibilidade, a qualidade dos argumentos e o
envolvimento com o consumidor têm um efeito positivo na credibilidade da informação.
A credibilidade da informação, por sua vez, tem um efeito positivo na atitude dos
consumidores perante o conteúdo partilhado. Verificou-se ainda que a atitude dos
consumidores tem um efeito positivo na intenção de visita de um destino turístico
publicitado, no WOM, na avaliação de alternativas e na satisfação com o influenciador.
Academicamente, este estudo colabora para a diminuição da escassez de
investigações sobre o tema, contribuindo com novos insights tanto do ponto de vista do
consumidor como do influenciador digital. Empresarialmente, contribui para o
desenvolvimento de estratégias digitais utilizando o marketing de influência, com
elementos que influenciem a intenção de compra de uma viagem. É sugerido que
futuramente sejam feitas adaptações do estudo, comparando outras redes sociais, ou
analisando outras características dos influenciadores que sejam valorizadas pelos.We live in a world where social networks are increasing their popularity, being
adopted both by people and companies including them in their strategies, keeping in mind
that influencer marketing is a relevant current term with great opportunities. Having said
that, digital influencers are an impacting factor in their followers’ lives and show a lot of
influence in the purchase intention process. Therefore, this study seeks to gain deeper
knowledge about the role that digital influencers play in consumers’ decision making,
particularly referring to travelling, by analyzing the characteristics of influencers that are
most valued by their followers, and how the consumer's attitude towards advertised tourist
destinations is affected.
The research is exploratory in nature, presenting a qualitative approach by
conducting in-depth interviews, and also explanatory, composing a quantitative approach
through an online questionnaire survey. Two in-depth interviews were conducted with
digital influencers in the travel field, based on Instagram. As a result, 104 valid responses
were obtained, founding a non-probabilistic and convenient sample.
After analyzing the results, it was observed that some characteristics of digital
influencers such as credibility, quality of arguments and engagement with the consumer
have a positive effect on the information credibility. The information credibility itself has
a positive effect on the consumers' attitude towards the shared content. It was also found
that consumer attitude has a positive effect on the intention to visit an advertised tourist
destination, WOM, evaluation of alternatives, and satisfaction with the digital influencer.
Academically, this study contributes to decreasing the scarcity of research on the
topic by contributing with new insights from both the consumer and digital influencer
perspectives. This topic is business relevant as it contributes to the development of digital
strategies using influencer marketing, with elements that influence the purchase intention
of a trip. It is suggested that future adaptations of the study be made, comparing it with
other social networks, or analyzing other characteristics of digital influencers that are
valued by their followers.info:eu-repo/semantics/publishedVersio
Crowd and AI Powered Manipulation: Characterization and Detection
User reviews are ubiquitous. They power online review aggregators that influence our daily-based decisions, from what products to purchase (e.g., Amazon), movies to view (e.g., Netflix,
HBO, Hulu), restaurants to patronize (e.g., Yelp), and hotels to book (e.g., TripAdvisor, Airbnb).
In addition, policy makers rely on online commenting platforms like Regulations.gov and FCC.gov as a means for citizens to voice their opinions about public policy issues. However, showcasing the opinions of fellow users has a dark side as these reviews and comments are vulnerable to manipulation. And as advances in AI continue, fake reviews generated by AI agents rather than users pose even more scalable and dangerous manipulation attacks. These attacks on online discourse can sway ratings of products, manipulate opinions and perceived support of key issues, and degrade our trust in online platforms. Previous efforts have mainly focused on highly visible anomaly behaviors captured by statistical modeling or clustering algorithms. While detection of such anomalous behaviors helps to improve the reliability of online interactions, it misses subtle and difficult-to-detect behaviors.
This research investigates two major research thrusts centered around manipulation strategies.
In the first thrust, we study crowd-based manipulation strategies wherein crowds of paid workers organize to spread fake reviews. In the second thrust, we explore AI-based manipulation strategies, where crowd workers are replaced by scalable, and potentially undetectable generative models of fake reviews. In particular, one of the key aspects of this work is to address the research gap in previous efforts for anomaly detection where ground truth data is missing (and hence, evaluation can be challenging). In addition, this work studies the capabilities and impact of model-based attacks as the next generation of online threats. We propose inter-related methods for collecting evidence of these attacks, and create new countermeasures for defending against them. The performance of proposed methods are compared against other state-of-the-art approaches in the literature. We find that although crowd campaigns do not show obvious anomaly behavior, they can be detected
given a careful formulation of their behaviors. And, although model-generated fake reviews may appear on the surface to be legitimate, we find that they do not completely mimic the underlying distribution of human-written reviews, so we can leverage this signal to detect them