Russo-Ukrainian War: Prediction and explanation of Twitter suspension

On 24 February 2022, Russia invaded Ukraine, starting what is now known as the Russo-Ukrainian War, initiating an online discourse on social media. Twitter as one of the most popular SNs, with an open and democratic character, enables a transparent discussion among its large user base. Unfortunately, this often leads to Twitter's policy violations, propaganda, abusive actions, civil integrity violation, and consequently to user accounts' suspension and deletion. This study focuses on the Twitter suspension mechanism and the analysis of shared content and features of the user accounts that may lead to this. Toward this goal, we have obtained a dataset containing 107.7M tweets, originating from 9.8 million users, using Twitter API. We extract the categories of shared content of the suspended accounts and explain their characteristics, through the extraction of text embeddings in junction with cosine similarity clustering. Our results reveal scam campaigns taking advantage of trending topics regarding the Russia-Ukrainian conflict for Bitcoin and Ethereum fraud, spam, and advertisement campaigns. Additionally, we apply a machine learning methodology including a SHapley Additive explainability model to understand and explain how user accounts get suspended

Promotional Campaigns in the Era of Social Platforms

The rise of social media has facilitated the diffusion of information to more easily reach millions of users. While some users connect with friends and organically share information and opinions on social media, others have exploited these platforms to gain influence and profit through promotional campaigns and advertising. The existence of promotional campaigns contributes to the spread of misleading information, spam, and fake news. Thus, these campaigns affect the trustworthiness and reliability of social media and render it as a crowd advertising platform. This dissertation studies the existence of promotional campaigns in social media and explores different ways users and bots (i.e. automated accounts) engage in such campaigns. In this dissertation, we design a suite of detection, ranking, and mining techniques. We study user-generated reviews in online e-commerce sites, such as Google Play, to extract campaigns. We identify cooperating sets of bots and classify their interactions in social networks such as Twitter, and rank the bots based on the degree of their malevolence. Our study shows that modern online social interactions are largely modulated by promotional campaigns such as political campaigns, advertisement campaigns, and incentive-driven campaigns. We measure how these campaigns can potentially impact information consumption of millions of social media users

Rethinking Privacy and Security Mechanisms in Online Social Networks

With billions of users, Online Social Networks(OSNs) are amongst the largest scale communication applications on the Internet. OSNs enable users to easily access news from local and worldwide, as well as share information publicly and interact with friends. On the negative side, OSNs are also abused by spammers to distribute ads or malicious information, such as scams, fraud, and even manipulate public political opinions. Having achieved significant commercial success with large amount of user information, OSNs do treat the security and privacy of their users seriously and provide several mechanisms to reinforce their account security and information privacy. However, the efficacy of those measures is either not thoroughly validated or in need to be improved. In sight of cyber criminals and potential privacy threats on OSNs, we focus on the evaluations and improvements of OSN user privacy configurations, account security protection mechanisms, and trending topic security in this dissertation. We first examine the effectiveness of OSN privacy settings on protecting user privacy. Given each privacy configuration, we propose a corresponding scheme to reveal the target user\u27s basic profile and connection information starting from some leaked connections on the user\u27s homepage. Based on the dataset we collected on Facebook, we calculate the privacy exposure in each privacy setting type and measure the accuracy of our privacy inference schemes with different amount of public information. The evaluation results show that (1) a user\u27s private basic profile can be inferred with high accuracy and (2) connections can be revealed in a significant portion based on even a small number of directly leaked connections. Secondly, we propose a behavioral-profile-based method to detect OSN user account compromisation in a timely manner. Specifically, we propose eight behavioral features to portray a user\u27s social behavior. A user\u27s statistical distributions of those feature values comprise its behavioral profile. Based on the sample data we collected from Facebook, we observe that each user\u27s activities are highly likely to conform to its behavioral profile while two different user\u27s profile tend to diverge from each other, which can be employed for compromisation detection. The evaluation result shows that the more complete and accurate a user\u27s behavioral profile can be built the more accurately compromisation can be detected. Finally, we investigate the manipulation of OSN trending topics. Based on the dataset we collected from Twitter, we manifest the manipulation of trending and a suspect spamming infrastructure. We then measure how accurately the five factors (popularity, coverage, transmission, potential coverage, and reputation) can predict trending using an SVM classifier. We further study the interaction patterns between authenticated accounts and malicious accounts in trending. at last we demonstrate the threats of compromised accounts and sybil accounts to trending through simulation and discuss countermeasures against trending manipulation

Popular with the Robots: Accusation and Automation in the Argentine Presidential Elections, 2015

Accusations of dishonourable campaigning have featured in every Argentine presidential election since the return to democracy in 1983. Yet, allegations made in the elections this October and November looked different from earlier ones. The campaign team for the centre-leftist candidate Daniel Scioli argued that Cambiemos, the centre-right coalition led by Mauricio Macri, was abusing the political affordances of social media by running a Twitter campaign via ‘50,000’ fake accounts. This paper presents evidence suggesting that both teams promoted their campaigns through automation on Twitter. Although the Macri campaign was subtler, both teams appear to have used automation to the same end: maximizing the diffusion of party content and creating an inflated image of their popularity. Neither team attempted to muffle or engage with opposing voices through automation. We argue that in a political culture fixated on the appearance of popularity, the use of automation to simulate mass support appears an organic development as campaigning enters the still unregulated Twittersphere. We compare our findings to the uses of automation in the Russian Twittersphere and conclude that there may be greater variation in the political usage of Twitter between political contexts than between different types of political event occurring in the same country.This study was funded by the Leverhulme Trust (grant number RP2012-C-017).This is the final version of the article. It first appeared from Springer via http://dx.doi.org/10.1007/s10767-016-9233-

Searching for associations between social media trending topics and organizations

This work focuses on how micro and small companies can take advantage of trending topics for marketing campaigns. Trending topics are the most discussed topics at the moment on social media platforms, particularly on Twitter and Facebook. While the access to trending topics is free and available to everyone, marketing specialists and specific software are more expensive, therefore small companies do not have the budget to support those costs. The main goal is to search for associations between trending topics and companies on social media platforms and HotRivers prototype is designed to accomplish this. A solution that aims to be inexpensive, fast, and automated. Detailed analyses were conducted to reduced the time and maximize the resources available at the lowest price. The final user receives a list of the trending topics related to the target company. For HotRivers were tested different pre-processing text techniques, a method to select tweets called Centroid Strategy and three models, an embedding vectors approach with Doc2Vec model, a probabilistic model with Latent Dirichlet Allocation, and a classification task approach with a Convolutional Neural Network used on the final architecture. The Centroid Strategy is used on trending topics to avoid unwanted tweets. In the results stand out that trending topic Nike has an association with the company Nike and #World- PatientSafetyDay has an association with Portsmouth Hospitals University. HotRivers cannot produce a full marketing campaign but can point out to the direction to the next campaign.Este trabalho foca-se na forma como as micro e pequenas empresas podem tirar partido dos trending topics para as suas campanhas de marketing. Os trending topics são os tópicos mais discutidos em cada momento nas redes sociais, particularmente no Twitter e no Facebook. Enquanto o acesso aos trending topics é gratuito e generalizado, os especialistas em marketing e o software especifico são dispendiosos, pelo que as pequenas empresas não têm o orçamento para suportar esses custos. O principal objetivo é procurar associações entre trending topics e empresas nas redes sociais e para isso foi criado um protótipo chamado HotRivers. Uma solução que pretende ser acessível, rápida e automatizada. Foram realizadas análises detalhadas para reduzir o tempo e maximizar os recursos disponíveis a preço baixo. O utilizador final recebe uma lista dos trending topics relacionados com a empresa alvo. O HotRivers foi testado com diferentes técnicas de pré-processamento de texto, um método para selecionar tweets chamado Estratégia Centroid e três modelos, uma abordagem de vectores embedding com o modelo Doc2Vec, um modelo probabilístico com Alocação de Dirichlet Latente, e uma abordagem de classificação com uma Rede Neural Convolucional, selecionada para a arquitetura final. A Estratégia Centroid é utilizada nos trending topics para evitar tweets indesejados. Nos resultados destacam-se o trending topic "Nike" que tem uma associação com a empresa Nike e #WorldPatientSafetyDay que tem uma associação com a Universidade dos Hospitais de Portsmouth. Embora o HotRivers não possa produzir uma campanha de marketing completa, pode apontar a direção para a campanha seguinte

Detecting Abnormal Behavior in Web Applications

The rapid advance of web technologies has made the Web an essential part of our daily lives. However, network attacks have exploited vulnerabilities of web applications, and caused substantial damages to Internet users. Detecting network attacks is the first and important step in network security. A major branch in this area is anomaly detection. This dissertation concentrates on detecting abnormal behaviors in web applications by employing the following methodology. For a web application, we conduct a set of measurements to reveal the existence of abnormal behaviors in it. We observe the differences between normal and abnormal behaviors. By applying a variety of methods in information extraction, such as heuristics algorithms, machine learning, and information theory, we extract features useful for building a classification system to detect abnormal behaviors.;In particular, we have studied four detection problems in web security. The first is detecting unauthorized hotlinking behavior that plagues hosting servers on the Internet. We analyze a group of common hotlinking attacks and web resources targeted by them. Then we present an anti-hotlinking framework for protecting materials on hosting servers. The second problem is detecting aggressive behavior of automation on Twitter. Our work determines whether a Twitter user is human, bot or cyborg based on the degree of automation. We observe the differences among the three categories in terms of tweeting behavior, tweet content, and account properties. We propose a classification system that uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot or cyborg. Furthermore, we shift the detection perspective from automation to spam, and introduce the third problem, namely detecting social spam campaigns on Twitter. Evolved from individual spammers, spam campaigns manipulate and coordinate multiple accounts to spread spam on Twitter, and display some collective characteristics. We design an automatic classification system based on machine learning, and apply multiple features to classifying spam campaigns. Complementary to conventional spam detection methods, our work brings efficiency and robustness. Finally, we extend our detection research into the blogosphere to capture blog bots. In this problem, detecting the human presence is an effective defense against the automatic posting ability of blog bots. We introduce behavioral biometrics, mainly mouse and keyboard dynamics, to distinguish between human and bot. By passively monitoring user browsing activities, this detection method does not require any direct user participation, and improves the user experience

Combating Threats to the Quality of Information in Social Systems

Many large-scale social systems such as Web-based social networks, online social media sites and Web-scale crowdsourcing systems have been growing rapidly, enabling millions of human participants to generate, share and consume content on a massive scale. This reliance on users can lead to many positive effects, including large-scale growth in the size and content in the community, bottom-up discovery of “citizen-experts”, serendipitous discovery of new resources beyond the scope of the system designers, and new social-based information search and retrieval algorithms. But the relative openness and reliance on users coupled with the widespread interest and growth of these social systems carries risks and raises growing concerns over the quality of information in these systems. In this dissertation research, we focus on countering threats to the quality of information in self-managing social systems. Concretely, we identify three classes of threats to these systems: (i) content pollution by social spammers, (ii) coordinated campaigns for strategic manipulation, and (iii) threats to collective attention. To combat these threats, we propose three inter-related methods for detecting evidence of these threats, mitigating their impact, and improving the quality of information in social systems. We augment this three-fold defense with an exploration of their origins in “crowdturfing” – a sinister counterpart to the enormous positive opportunities of crowdsourcing. In particular, this dissertation research makes four unique contributions: • The first contribution of this dissertation research is a framework for detecting and filtering social spammers and content polluters in social systems. To detect and filter individual social spammers and content polluters, we propose and evaluate a novel social honeypot-based approach. • Second, we present a set of methods and algorithms for detecting coordinated campaigns in large-scale social systems. We propose and evaluate a content- driven framework for effectively linking free text posts with common “talking points” and extracting campaigns from large-scale social systems. • Third, we present a dual study of the robustness of social systems to collective attention threats through both a data-driven modeling approach and deploy- ment over a real system trace. We evaluate the effectiveness of countermeasures deployed based on the first moments of a bursting phenomenon in a real system. • Finally, we study the underlying ecosystem of crowdturfing for engaging in each of the three threat types. We present a framework for “pulling back the curtain” on crowdturfers to reveal their underlying ecosystem on both crowdsourcing sites and social media