Examining Political Discourse on Online 8Kun and Reddit Forums

A recent example of political violence in the United States was that of the January 6, 2021, Capitol attack in connection with the certification of Joseph R. Biden’s victory over Donald J. Trump in the 2020 US presidential election. This thesis analyzes the events of January 6, 2021, through the lens of social media discourse. This thesis presents a workflow that acquired over 5 million 8kun and Reddit posts from various apolitical and political forums in the three months preceding and following the Capitol attack on January 6, 2021. Techniques from text analysis are then used to group forums according to the similarities of their posting patterns. Five main groups of forums are identified. Finally, this thesis analyzes these forums for feelings of isolation and displacement from society in connection with the events of January 6, 2021. Such feelings were not clearly identified. This thesis demonstrates the challenges and opportunities of scraping and analyzing social media data

Aplicação de técnicas de Clustering ao contexto da Tomada de Decisão em Grupo

Nowadays, decisions made by executives and managers are primarily made in a group. Therefore, group decision-making is a process where a group of people called participants work together to analyze a set of variables, considering and evaluating a set of alternatives to select one or more solutions. There are many problems associated with group decision-making, namely when the participants cannot meet for any reason, ranging from schedule incompatibility to being in different countries with different time zones. To support this process, Group Decision Support Systems (GDSS) evolved to what today we call web-based GDSS. In GDSS, argumentation is ideal since it makes it easier to use justifications and explanations in interactions between decision-makers so they can sustain their opinions. Aspect Based Sentiment Analysis (ABSA) is a subfield of Argument Mining closely related to Natural Language Processing. It intends to classify opinions at the aspect level and identify the elements of an opinion. Applying ABSA techniques to Group Decision Making Context results in the automatic identification of alternatives and criteria, for example. This automatic identification is essential to reduce the time decision-makers take to step themselves up on Group Decision Support Systems and offer them various insights and knowledge on the discussion they are participants. One of these insights can be arguments getting used by the decision-makers about an alternative. Therefore, this dissertation proposes a methodology that uses an unsupervised technique, Clustering, and aims to segment the participants of a discussion based on arguments used so it can produce knowledge from the current information in the GDSS. This methodology can be hosted in a web service that follows a micro-service architecture and utilizes Data Preprocessing and Intra-sentence Segmentation in addition to Clustering to achieve the objectives of the dissertation. Word Embedding is needed when we apply clustering techniques to natural language text to transform the natural language text into vectors usable by the clustering techniques. In addition to Word Embedding, Dimensionality Reduction techniques were tested to improve the results. Maintaining the same Preprocessing steps and varying the chosen Clustering techniques, Word Embedders, and Dimensionality Reduction techniques came up with the best approach. This approach consisted of the KMeans++ clustering technique, using SBERT as the word embedder with UMAP dimensionality reduction, reducing the number of dimensions to 2. This experiment achieved a Silhouette Score of 0.63 with 8 clusters on the baseball dataset, which wielded good cluster results based on their manual review and Wordclouds. The same approach obtained a Silhouette Score of 0.59 with 16 clusters on the car brand dataset, which we used as an approach validation dataset.Atualmente, as decisões tomadas por gestores e executivos são maioritariamente realizadas em grupo. Sendo assim, a tomada de decisão em grupo é um processo no qual um grupo de pessoas denominadas de participantes, atuam em conjunto, analisando um conjunto de variáveis, considerando e avaliando um conjunto de alternativas com o objetivo de selecionar uma ou mais soluções. Existem muitos problemas associados ao processo de tomada de decisão, principalmente quando os participantes não têm possibilidades de se reunirem (Exs.: Os participantes encontramse em diferentes locais, os países onde estão têm fusos horários diferentes, incompatibilidades de agenda, etc.). Para suportar este processo de tomada de decisão, os Sistemas de Apoio à Tomada de Decisão em Grupo (SADG) evoluíram para o que hoje se chamam de Sistemas de Apoio à Tomada de Decisão em Grupo baseados na Web. Num SADG, argumentação é ideal pois facilita a utilização de justificações e explicações nas interações entre decisores para que possam suster as suas opiniões. Aspect Based Sentiment Analysis (ABSA) é uma área de Argument Mining correlacionada com o Processamento de Linguagem Natural. Esta área pretende classificar opiniões ao nível do aspeto da frase e identificar os elementos de uma opinião. Aplicando técnicas de ABSA à Tomada de Decisão em Grupo resulta na identificação automática de alternativas e critérios por exemplo. Esta identificação automática é essencial para reduzir o tempo que os decisores gastam a customizarem-se no SADG e oferece aos mesmos conhecimento e entendimentos sobre a discussão ao qual participam. Um destes entendimentos pode ser os argumentos a serem usados pelos decisores sobre uma alternativa. Assim, esta dissertação propõe uma metodologia que utiliza uma técnica não-supervisionada, Clustering, com o objetivo de segmentar os participantes de uma discussão com base nos argumentos usados pelos mesmos de modo a produzir conhecimento com a informação atual no SADG. Esta metodologia pode ser colocada num serviço web que segue a arquitetura micro serviços e utiliza Preprocessamento de Dados e Segmentação Intra Frase em conjunto com o Clustering para atingir os objetivos desta dissertação. Word Embedding também é necessário para aplicar técnicas de Clustering a texto em linguagem natural para transformar o texto em vetores que possam ser usados pelas técnicas de Clustering. Também Técnicas de Redução de Dimensionalidade também foram testadas de modo a melhorar os resultados. Mantendo os passos de Preprocessamento e variando as técnicas de Clustering, Word Embedder e as técnicas de Redução de Dimensionalidade de modo a encontrar a melhor abordagem. Essa abordagem consiste na utilização da técnica de Clustering KMeans++ com o SBERT como Word Embedder e UMAP como a técnica de redução de dimensionalidade, reduzindo as dimensões iniciais para duas. Esta experiência obteve um Silhouette Score de 0.63 com 8 clusters no dataset de baseball, que resultou em bons resultados de cluster com base na sua revisão manual e visualização dos WordClouds. A mesma abordagem obteve um Silhouette Score de 0.59 com 16 clusters no dataset das marcas de carros, ao qual usamos esse dataset com validação de abordagem

Private Web Search with Constant Round Efficiency

Web search is increasingly becoming an essential activity as it is frequently the most effective and convenient way of finding information. However, it can be a threat for the privacy of users because their queries may reveal their sensitive information. Private web search (PWS) solutions allow users to find information in the Internet while preserving their privacy. In particular, cryptography-based PWS (CB-PWS) systems provide strong privacy guarantees. This paper introduces a constant-round CB-PWS protocol which remains computationally efficient, compared to known CB-PWS systems. Our construction is comparable to similar solutions regarding users\u27 privacy

Aquilis: Using Contextual Integrity for Privacy Protection on Mobile Devices

Peer reviewe

Online Privacy in Mobile and Web Platforms: Risk Quantification and Obfuscation Techniques

The wide-spread use of the web and mobile platforms and their high engagement in human lives pose serious threats to the privacy and confidentiality of users. It has been demonstrated in a number of research works that devices, such as desktops, mobile, and web browsers contain subtle information and measurable variation, which allow them to be fingerprinted. Moreover, behavioural tracking is another form of privacy threat that is induced by the collection and monitoring of users gestures such as touch, motion, GPS, search queries, writing pattern, and more. The success of these methods is a clear indication that obfuscation techniques to protect the privacy of individuals, in reality, are not successful if the collected data contains potentially unique combinations of attributes relating to specific individuals. With this in view, this thesis focuses on understanding the privacy risks across the web and mobile platforms by identifying and quantifying the privacy leakages and then designing privacy preserving frameworks against identified threats. We first investigate the potential of using touch-based gestures to track mobile device users. For this purpose, we propose and develop an analytical framework that quantifies the amount of information carried by the user touch gestures. We then quantify users privacy risk in the web data using probabilistic method that incorporates all key privacy aspects, which are uniqueness, uniformity, and linkability of the web data. We also perform a large-scale study of dependency chains in the web and find that a large proportion of websites under-study load resources from suspicious third-parties that are known to mishandle user data and risk privacy leaks. The second half of the thesis addresses the abovementioned identified privacy risks by designing and developing privacy preserving frameworks for the web and mobile platforms. We propose an on-device privacy preserving framework that minimizes privacy leakages by bringing down the risk of trackability and distinguishability of mobile users while preserving the functionality of the existing apps/services. We finally propose a privacy-aware obfuscation framework for the web data having high predicted risk. Using differentially-private noise addition, our proposed framework is resilient against adversary who has knowledge about the obfuscation mechanism, HMM probabilities and the training dataset

Privacy protection of user profiles in personalized information systems

In recent times we are witnessing the emergence of a wide variety of information systems that tailor the information-exchange functionality to meet the specific interests of their users. Most of these personalized information systems capitalize on, or lend themselves to, the construction of profiles, either directly declared by a user, or inferred from past activity. The ability of these systems to profile users is therefore what enables such intelligent functionality, but at the same time, it is the source of serious privacy concerns. Although there exists a broad range of privacy-enhancing technologies aimed to mitigate many of those concerns, the fact is that their use is far from being widespread. The main reason is that there is a certain ambiguity about these technologies and their effectiveness in terms of privacy protection. Besides, since these technologies normally come at the expense of system functionality and utility, it is challenging to assess whether the gain in privacy compensates for the costs in utility. Assessing the privacy provided by a privacy-enhancing technology is thus crucial to determine its overall benefit, to compare its effectiveness with other technologies, and ultimately to optimize it in terms of the privacy-utility trade-off posed. Considerable effort has consequently been devoted to investigating both privacy and utility metrics. However, most of these metrics are specific to concrete systems and adversary models, and hence are difficult to generalize or translate to other contexts. Moreover, in applications involving user profiles, there are a few proposals for the evaluation of privacy, and those existing are not appropriately justified or fail to justify the choice. The first part of this thesis approaches the fundamental problem of quantifying user privacy. Firstly, we present a theoretical framework for privacy-preserving systems, endowed with a unifying view of privacy in terms of the estimation error incurred by an attacker who aims to disclose the private information that the system is designed to conceal. Our theoretical analysis shows that numerous privacy metrics emerging from a broad spectrum of applications are bijectively related to this estimation error, which permits interpreting and comparing these metrics under a common perspective. Secondly, we tackle the issue of measuring privacy in the enthralling application of personalized information systems. Specifically, we propose two information-theoretic quantities as measures of the privacy of user profiles, and justify these metrics by building on Jaynes' rationale behind entropy-maximization methods and fundamental results from the method of types and hypothesis testing. Equipped with quantifiable measures of privacy and utility, the second part of this thesis investigates privacy-enhancing, data-perturbative mechanisms and architectures for two important classes of personalized information systems. In particular, we study the elimination of tags in semantic-Web applications, and the combination of the forgery and the suppression of ratings in personalized recommendation systems. We design such mechanisms to achieve the optimal privacy-utility trade-off, in the sense of maximizing privacy for a desired utility, or vice versa. We proceed in a systematic fashion by drawing upon the methodology of multiobjective optimization. Our theoretical analysis finds a closed-form solution to the problem of optimal tag suppression, and to the problem of optimal forgery and suppression of ratings. In addition, we provide an extensive theoretical characterization of the trade-off between the contrasting aspects of privacy and utility. Experimental results in real-world applications show the effectiveness of our mechanisms in terms of privacy protection, system functionality and data utility

Privacy Enhancing Technologies for solving the privacy-personalization paradox : taxonomy and survey

Personal data are often collected and processed in a decentralized fashion, within different contexts. For instance, with the emergence of distributed applications, several providers are usually correlating their records, and providing personalized services to their clients. Collected data include geographical and indoor positions of users, their movement patterns as well as sensor-acquired data that may reveal users’ physical conditions, habits and interests. Consequently, this may lead to undesired consequences such as unsolicited advertisement and even to discrimination and stalking. To mitigate privacy threats, several techniques emerged, referred to as Privacy Enhancing Technologies, PETs for short. On one hand, the increasing pressure on service providers to protect users’ privacy resulted in PETs being adopted. One the other hand, service providers have built their business model on personalized services, e.g. targeted ads and news. The objective of the paper is then to identify which of the PETs have the potential to satisfy both usually divergent - economical and ethical - purposes. This paper identifies a taxonomy classifying eight categories of PETs into three groups, and for better clarity, it considers three categories of personalized services. After defining and presenting the main features of PETs with illustrative examples, the paper points out which PETs best fit each personalized service category. Then, it discusses some of the inter-disciplinary privacy challenges that may slow down the adoption of these techniques, namely: technical, social, legal and economic concerns. Finally, it provides recommendations and highlights several research directions

Gender mainstreaming in Polish media discourse : as exemplified in the debate concerning the Convention on preventing and combating violence against women and domestic violence

Praca recenzowana / Peer-reviewed paperPublikacja, którą oddajemy do rąk czytelników i czytelniczek, jest pokłosiem projektu Gender mainstreaming w polskim dyskursie medialnym, realizowanym w latach 2012–2013 przez zespół pracowników Katedry Systemów Medialnych i Komunikowania Politycznego Wydziału Zarządzania i Komunikacji Społecznej Krakowskiej Akademii im. Andrzeja Frycza Modrzewskiego. Celem projektu było zbadanie, w jakim stopniu polityka równości płci i strategia włączania zagadnień płci do głównego nurtu, będąca postulatem Deklaracji pekińskiej, Traktatu amsterdamskiego, a także wielu szczegółowych dokumentów Organizacji Narodów Zjednoczonych, Unii Europejskiej i Rady Europy, znajduje odzwierciedlenie w polskim dyskursie medialnym. Kiedy na początku 2012 roku rozpoczynaliśmy pracę nad projektem, nie przypuszczaliśmy, że rzeczywistość napisze do niego scenariusz niemieszczący się pierwotnie w naszych planach, który skierował badania prowadzone przez zespół na nowe tory

4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 4th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges. Due to the covid pandemic, CARMA 2022 is planned as a virtual and face-to-face conference, simultaneouslyDoménech I De Soria, J.; Vicente Cuervo, MR. (2022). 4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2022.2022.1595