138 research outputs found
Metaverse. Old urban issues in new virtual cities
Recent years have seen the arise of some early attempts to build virtual cities,
utopias or affective dystopias in an embodied Internet, which in some respects appear to
be the ultimate expression of the neoliberal city paradigma (even if virtual). Although
there is an extensive disciplinary literature on the relationship between planning and
virtual or augmented reality linked mainly to the gaming industry, this often avoids design
and value issues. The observation of some of these early experiences - Decentraland,
Minecraft, Liberland Metaverse, to name a few - poses important questions and problems
that are gradually becoming inescapable for designers and urban planners, and allows
us to make some partial considerations on the risks and potentialities of these early virtual
cities
Recommender Systems for Online and Mobile Social Networks: A survey
Recommender Systems (RS) currently represent a fundamental tool in online
services, especially with the advent of Online Social Networks (OSN). In this
case, users generate huge amounts of contents and they can be quickly
overloaded by useless information. At the same time, social media represent an
important source of information to characterize contents and users' interests.
RS can exploit this information to further personalize suggestions and improve
the recommendation process. In this paper we present a survey of Recommender
Systems designed and implemented for Online and Mobile Social Networks,
highlighting how the use of social context information improves the
recommendation task, and how standard algorithms must be enhanced and optimized
to run in a fully distributed environment, as opportunistic networks. We
describe advantages and drawbacks of these systems in terms of algorithms,
target domains, evaluation metrics and performance evaluations. Eventually, we
present some open research challenges in this area
Robust Recommender System: A Survey and Future Directions
With the rapid growth of information, recommender systems have become
integral for providing personalized suggestions and overcoming information
overload. However, their practical deployment often encounters "dirty" data,
where noise or malicious information can lead to abnormal recommendations.
Research on improving recommender systems' robustness against such dirty data
has thus gained significant attention. This survey provides a comprehensive
review of recent work on recommender systems' robustness. We first present a
taxonomy to organize current techniques for withstanding malicious attacks and
natural noise. We then explore state-of-the-art methods in each category,
including fraudster detection, adversarial training, certifiable robust
training against malicious attacks, and regularization, purification,
self-supervised learning against natural noise. Additionally, we summarize
evaluation metrics and common datasets used to assess robustness. We discuss
robustness across varying recommendation scenarios and its interplay with other
properties like accuracy, interpretability, privacy, and fairness. Finally, we
delve into open issues and future research directions in this emerging field.
Our goal is to equip readers with a holistic understanding of robust
recommender systems and spotlight pathways for future research and development
Analyzing Social Media Data using Sentiment Mining and Bi-gram Analysis for the Recommendation of YouTube Videos.
In this work we combine sentiment analysis with graph theory to analyze user posts, likes/dislikes on a variety of social media to provide recommendations for YouTube videos. We focus on the topic of climate change/global warming which has caused much alarm and controversy over recent years. Our intention is to recommend informative YouTube videos to those seeking a balanced viewpoint of this area and the key arguments/issues. To this end we analyze Twitter data; Reddit comments and posts; user comments, view statistics and likes/dislikes of YouTube videos. The combination of sentiment analysis with raw statistics and linking users with their posts gives deeper insights into their needs and quest for quality information. Sentiment analysis provides the insights into user likes and dislikes, graph theory provides the linkage patterns and relationships between users, posts and sentiment
Popularity Bias as Ethical and Technical Issue in Recommendation: A Survey
Recommender Systems have become omnipresent in our ev- eryday life, helping us making decisions and navigating in the digital world full of information. However, only recently researchers have started discovering undesired and harmful effects of automated recommendation and began questioning how fair and ethical these systems are, while in- fluencing our day-to-day decision making, shaping our online behaviour and tastes. In the latest research works, various biases and phenomena like filter bubbles and echo chambers have been uncovered among the resulting effects of recommender systems and rigorous work has started on solving these issues. In this narrative survey, we investigate the emer- gence and progression of research on one of the potential types of biases in recommender systems, i.e. Popularity Bias. Many recommender al- gorithms have been shown to favor already popular items, hence giving them even more exposure, which can harm fairness and diversity on the platforms using such systems. Such a problem becomes even more com- plicated if the object of recommendation is not just products and content, but people, their work and services. This survey describes the progress in this field of study, highlighting the advancements and identifying the gaps in the research, where additional effort and attention is necessary to minimize the harmful effect and make sure that such systems are build in a fair and ethical way
A Survey on Popularity Bias in Recommender Systems
Recommender systems help people find relevant content in a personalized way.
One main promise of such systems is that they are able to increase the
visibility of items in the long tail, i.e., the lesser-known items in a
catalogue. Existing research, however, suggests that in many situations today's
recommendation algorithms instead exhibit a popularity bias, meaning that they
often focus on rather popular items in their recommendations. Such a bias may
not only lead to limited value of the recommendations for consumers and
providers in the short run, but it may also cause undesired reinforcement
effects over time. In this paper, we discuss the potential reasons for
popularity bias and we review existing approaches to detect, quantify and
mitigate popularity bias in recommender systems. Our survey therefore includes
both an overview of the computational metrics used in the literature as well as
a review of the main technical approaches to reduce the bias. We furthermore
critically discuss today's literature, where we observe that the research is
almost entirely based on computational experiments and on certain assumptions
regarding the practical effects of including long-tail items in the
recommendations.Comment: Under review, submitted to UMUA
Regulatory Annexation and the Matrix of Dependence: The Regulation of Social Media in Nigeria
This research addresses social media regulation targeted at users in Nigeria, while also considering issues related to the regulation and governance of social media and new media technologies across the world. This includes debates over online safety versus freedom of expression, platform power versus state influence, and structural inequalities that exist between the Global North and South in terms of the use, design, and regulation of new media technologies. The thesis centres around political economy and theoretical insights drawn from studies into internet and social media regulation, the securitisation of online harms, and practical approaches to regulating social media content. The analysis is based on a methodology that combines policy analysis, case study, interview, and social media analysis to explore how social media regulation can be understood from the standpoint of policy, politics, opposition, and alternatives. Based on these, the study argues that social media regulation in Nigeria mirrors broadcasting regulation in what I call regulatory annexation, given the matrix of dependence that relegates the Global South to regulatory decisions made by governments and platforms in the Global North.
To establish this argument, I define the matrix of dependence as Nigeria’s reliance on the West for new media regulatory outcomes of virtually any kind. Platformatisation further places Nigeria on the disadvantaged side of a balance of power with global tech platforms. The country, therefore, turns to users, intending to maintain on social media the same level of control it wields over the traditional media – a concept that I introduce for the first time as regulatory annexation. This results in the opposition that users deploy on Twitter, the central platform for activist discourse, using othering tactics that often shape state-citizen relations in Nigeria. I conclude the thesis by suggesting the need for research that expands on regulatory annexation and the matrix of dependence, focusing on the implications that they portend for regulatory interventions in other contexts, particularly in the Global South, the kind of regulation that is more likely to target users
Single-User Injection for Invisible Shilling Attack against Recommender Systems
Recommendation systems (RS) are crucial for alleviating the information
overload problem. Due to its pivotal role in guiding users to make decisions,
unscrupulous parties are lured to launch attacks against RS to affect the
decisions of normal users and gain illegal profits. Among various types of
attacks, shilling attack is one of the most subsistent and profitable attacks.
In shilling attack, an adversarial party injects a number of well-designed fake
user profiles into the system to mislead RS so that the attack goal can be
achieved. Although existing shilling attack methods have achieved promising
results, they all adopt the attack paradigm of multi-user injection, where some
fake user profiles are required. This paper provides the first study of
shilling attack in an extremely limited scenario: only one fake user profile is
injected into the victim RS to launch shilling attacks (i.e., single-user
injection). We propose a novel single-user injection method SUI-Attack for
invisible shilling attack. SUI-Attack is a graph based attack method that
models shilling attack as a node generation task over the user-item bipartite
graph of the victim RS, and it constructs the fake user profile by generating
user features and edges that link the fake user to items. Extensive experiments
demonstrate that SUI-Attack can achieve promising attack results in single-user
injection. In addition to its attack power, SUI-Attack increases the
stealthiness of shilling attack and reduces the risk of being detected. We
provide our implementation at: https://github.com/KDEGroup/SUI-Attack.Comment: CIKM 2023. 10 pages, 5 figure
Practical Cross-system Shilling Attacks with Limited Access to Data
In shilling attacks, an adversarial party injects a few fake user profiles
into a Recommender System (RS) so that the target item can be promoted or
demoted. Although much effort has been devoted to developing shilling attack
methods, we find that existing approaches are still far from practical. In this
paper, we analyze the properties a practical shilling attack method should have
and propose a new concept of Cross-system Attack. With the idea of Cross-system
Attack, we design a Practical Cross-system Shilling Attack (PC-Attack)
framework that requires little information about the victim RS model and the
target RS data for conducting attacks. PC-Attack is trained to capture graph
topology knowledge from public RS data in a self-supervised manner. Then, it is
fine-tuned on a small portion of target data that is easy to access to
construct fake profiles. Extensive experiments have demonstrated the
superiority of PC-Attack over state-of-the-art baselines. Our implementation of
PC-Attack is available at https://github.com/KDEGroup/PC-Attack.Comment: Accepted by AAAI 202
Myth and conspiracy among the alt-right : a five-year observation of 4chan’s /pol/
This thesis is the culmination of a five-year observation of the neo-fascist community on 4chan’s /pol/ forum. This observation began at the end of 2016, and continued until the end of 2021, a period which included many key moments in the alt-right’s history, such as the period of confidence following Donald Trump’s election, the fallout of the Unite the Right rally in 2017, and the turn toward terrorism in 2019. Drawing upon this observation, it describes the unique mechanisms present on /pol/ that support radicalisation, and analyses how the use of a mythic narrative binds the movement together and explains their actions. It is not an exhaustive description of the movement or its beliefs – this territory has been covered by others – but instead focuses on using dissecting the stories that underpin the movement’s unique and deeply unusual view of reality. It discusses their conspiracy theories in depth, along with their mythic relationship to the fascisms of the past, and their apocalyptic visions of the future
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