Web Science embraces the study of the Web as a vast information network of people and communities. It also includes the study of people and communities by using the digital records of user activity mediated by the Web. An understanding of human behavior and social interaction can contribute to our understanding of the Web, and data obtained from the Web can contribute to our understanding of human behavior and social interaction. Accordingly, Web Science involves analysis and design of Web architecture and applications, as well as studies of the people, organizations, and policies that shape and are shaped by the Web. To address these diverse goals, The Journal of Web Science is inherently interdisciplinary, integrating computer and information sciences, communication, linguistics, sociology, psychology, economics, law, political science, and other disciplines. The Journal of Web Science is unique in the manner in which it brings these disciplines together in creative and critical dialogue. We therefore invite research papers describing original research from all the above disciplines, as well as those that cross traditional disciplinary boundaries. In order to best serve the community, The Journal of Web Science will provide immediate open access to its content. Accepted papers will be published online immediately upon acceptance. The copyright of the published articles stays with the authors. It is free for authors as well as for readers.
The copyright of the published articles stays with the authors.
Toward Understanding Online Impression Management: How Twitter Users Control Textual Expressions Over Time
Impression management on social networking sites is becoming more important as people live in an increasingly connected world where they initialize, develop, and maintain relationships with others online. Previous studies have shown that people form impressions differently depending on their relationship with their audience. However, few studies have focused on the longitudinal aspect of how people manage their impressions by controlling their expressions over time according to the audience. In this study, we investigated temporal changes in textual expressions (e.g., neurotic words) and then analyzed how such changes were related to a person’s audience size (i.e., followers), density (i.e., mutual connections), and feedback (e.g., Likes). An analysis of 5 million posts collected from 1.6 thousand Twitter users over a period of 2.5 years revealed that users who had developed more mutual connections with their audience tended to use more neurotic and conscientious expressions. Meanwhile, users who received more Likes from their audience wrote fewer neurotic or conscientious expressions. Our findings indicate that Twitter users gradually adjust their use of expressions through their interactions with audiences, which may ultimately change the impressions that others have of them.
Copyright (c) 2022 Tomu Tominaga, Yoshinori Hijikata, Naomi Yamashita
Characterizing the country-wide adoption and evolution of the Jodel messaging app in Saudi Arabia
Social media is subject to constant growth and evolution, yet little is known about their early phases of adoption. To shed light on this aspect, this paper empirically characterizes the initial and country-wide adoption of a new type of social media in Saudi Arabia that happened in 2017. Unlike established social media, the studied network is anonymous and location-based to form hundreds of independent communities country-wide whose adoption pattern we compare nation-wide. We take a detailed and full view from the operators perspective on the temporal and geographical dimension on the evolution of these different communities---from their very first the first months of establishment to saturation. This way, we make the early adoption of a new type of social media visible, a process that is often invisible due to the lack of data covering the first days of a new network.
Copyright (c) 2022 Jens Helge Reelfs, Oliver Hohlfeld, Markus Strohmaier, Niklas Henckell
Privacy and Social Media Use in the Arabian Gulf: Saudi Arabian & Qatari Traditional Values in the Digital World
We explore the meaning of “privacyâ€ among citizens of the Gulf states of Qatar and Saudi Arabia as it manifests in digital environments. Privacy is an essential and widely respected value in many cultures, and the way in which it is understood and enacted depends on context. Therefore, we conduct this research to understand user behaviors regarding privacy in the digital sphere, where individuals increasingly publish personal information. We present the results of two Arab Gulf based-studies, the first one is a mixed-methods analysis of 18K Twitter posts of Qatari nationals that mention “privacy.â€ The second study provides qualitative insights through 34 ethnographically-informed interviews that focus on online privacy with Saudi Arabian nationals. In our analysis, we pay attention to the face-to-face and digital contexts in which privacy is mentioned and enacted, and how those contexts lead to varied ideologies regarding privacy. Our findings reveal that in this context, the need and pursuit of privacy stems from the Islamic faith, in addition to the use of paternalistic language by men when discussing women’s privacy is common. Above all, privacy is framed as a communal attribute, including not only the individual, but the behavior of those around them; it even extends beyond one’s lifespan. We contribute an analysis and description of these previously unexplored interpretations of privacy, which play a role in how users navigate social media.
Rise and Fall of reputation in a Web of Trust: the Bitcoin-OTC market case
Bitcoin-otc is a peer to peer (over-the-counter) marketplace for trading with bitcoin crypto-currency. To mitigate the risks of the p2p unsupervised exchanges, the establishment of a reliable reputation systems is needed: for this reason, a web of trust is implemented on the website. The availability of all the historic of the users' interaction data makes this dataset a unique playground for studying reputation dynamics through others' evaluations. We analyze the structure and the dynamics of this web of trust with a multilayer network approach distinguishing the rewarding and the punitive behaviors. We show that the rewarding and the punitive behavior have similar emergent topological properties (apart from the clustering coefficient being higher for the rewarding layer) and that the resultant reputation originates from the complex interaction of the more regular behaviors on the layers. We show that the systems' reputation inequality reaches a high steady value with the network evolution. We characterize the reputation trajectories identifying prototypical behaviors associated to three classes of users: trustworthy, untrusted and controversial. Controversial users are the only ones presenting up and down reputation trends. We focus on these cases for understanding which are the possible factors driving reputation falls and which dynamical patterns characterize these cascades: some users have real oscillating behaviors, other abuse of the trust system doing a few good transactions to gain reputation for cheating the users afterwards, other naturally and slowly die out after a long series of positive exchanges (like disappearing from the system) and finally, some users are hardly beaten by organized trolling attacks.
Copyright (c) 2019 Floriana Gargiulo, Ilaria Bertazzi, Sylvie Huet
Angel or Demon? Characterizing Variations Across Twitter Timeline of Technical Support Campaigners
Technical Support spam, which abuse Web 2.0 and carry out social engineering attacks have been in existence for a very long time, despite several measures taken to thwart such attacks. Although recent research has looked into unveiling tactics employed by spammers to lure victims, damage done on Online Social Networks is largely unexplored. In this paper, we perform the first large-scale study to understand the behavior of technical support spammers, and compare them with the legitimate technical support offered to OSN users by several brands such as Microsoft, Facebook, Amazon. We analyze the spam and legitimate accounts over a period of 20 months, and provide a taxonomy of the different types of spammers that are active in Tech Support spam landscape. We develop an automated mechanism to classify spammers from legitimate accounts, achieving a precision, recall of 99.8%. Our results shed light on the threats associated with billions of users using OSNs from Tech Support spam, and can help researchers and OSN service providers in developing effective countermeasures to fight them.
Copyright (c) 2019 Srishti Gupta, Gurpreet Singh Bhatia, Saksham Suri, Dhruv Kuchhal, Payas Gupta, Mustaque Ahamad, Manish Gupta, Ponnurangam Kumaraguru
Different Topic, Different Traffic: How Search and Navigation Interplay on Wikipedia
As one of the richest sources of encyclopedic information on the Web, Wikipedia offers large-scale article access data that allows us to compare articles with respect to the two main paradigms of information seeking, i.e., search by formulating a query, and navigation by following hyperlinks. Using such data from the English Wikipedia, we study access behavior by employing two main metrics, namely (i) searchshare -- the relative amount of views an article received by search --, and (ii) resistance -- the ability of an article to relay traffic to other Wikipedia articles -- to characterize articles. We demonstrate how articles in distinct topical categories differ substantially in terms of these properties. For example, architecture-related articles are often accessed through search and are simultaneously a ``dead end'' for traffic, whereas historical articles about military events are mainly navigated. We further link traffic differences to varying network, content, and editing activity features. Lastly, we measure the impact of the article properties by modeling access behavior on articles with a gradient boosting approach and explore explicit importance of individual features. Our results constitute a step towards understanding human information seeking behavior, and may contribute to identify focal points for future improvements of Wikipedia and similar systems.
Radicalisation Influence in Social Media
In an increasingly digital world, identifying signs of online extremism sits at the top of the priority list for counter-extremist agencies. Researchers and governments are investing in the creation of advanced information technologies to identify and counter extremism through intelligent large-scale analysis of online data. However, to the best of our knowledge, these technologies are neither based on, nor do they take advantage of, the existing theories and studies of radicalisation. In this paper we propose a computational approach for detecting and predicting the radicalisation in uence a user is exposed to, grounded on the notion of ’roots of radicalisation’ from social science models. This approach has been applied to analyse and compare the radicalisation level of 112 pro-ISIS vs.112 “general" Twitter users. Our results show the e ectiveness of our proposed algorithms in detecting and predicting radicalisation in uence, ob- taining up to 0.9 F-1 measure for detection and between 0.7 and 0.8 precision for prediction. We have also conducted an in-depth anal- ysis of the social in uence received by the 112 pro-ISIS accounts, and reported on the origin, frequency and topical diversity of this in uence. While this is an initial attempt towards the e ective com- bination of social and computational perspectives, more work is needed to bridge these disciplines, and to build on their strengths to target the problem of online radicalisation
Pro-Environmental Campaigns via Social Media: Analysing Awareness and Behaviour Patterns
Changing people's behaviour with regards to energy consumption is often regarded as key to mitigating climate change. To this end, endless campaigns have been run by governments and environmental organisations to engage and raise awareness of the public, and to promote behaviour change. Nowadays, many of such campaigns expand to social media, in the hope of increasing their reach and impact. However, and in spite of persistent efforts, public engagement with these campaigns tend to be rather underwhelming. This demonstrates the need for adopting new strategies in designing and executing these campaigns. To the best of our knowledge, these campaigns often overlook existing theories and studies on user engagement and behaviour change. To close this gap, this paper uses Robinson's Five Door Theory of behaviour change \cite{robinson5doors} to analyse online user behaviour towards climate change. With this approach, users's behavioural stages can be automatically identified from their contributions on social media. We apply this approach to analyse the behaviour of participants in three global campaigns on Twitter; United Nations COP21, Earth Hour 2015, and Earth Hour 2016. Our results provide guidelines on how to improve communication during these online campaigns to increase public engagement and participation.
Copyright (c) 2017 Miriam Fernandez, Lara Piccolo, Harith Alani, Diana Maynard, Christop Meili, Meia Wippoo
Towards an Understanding of Digital Disaster Response Workflow in Humanitarian Emergencies
This paper examines the workflow of digital volunteers during disaster response operations. It reveals how digital volunteers go through sense-making activities to filter crowdsourced information to warrant confidence that the data satisfies the standard of engagement, production and analysis. We do so by studying a digital disaster response organisation - Humanity Road - through fifteen response operations across thirteen countries using digital ethnography over a period of sixteen months. This is a qualitative study using virtual ethnographic method. The data collection of this study incorporates a diverse range of sources such as Skype chat logs, field notes, social media postings, and official documents. Based on the findings of this study we proposed a framework that offers structured workflow for the communities of practice within the domain of Digital Humanitarian Networks. Our findings suggest practical implications for both the digital humanitarian organisations and governments of the disaster-prone countries.
Copyright (c) 2019 Najeeb Gambo Abdulhamid
Modeling Dynamic Ideological Behavior in Political Networks
In this article, we model and analyze the dynamic behavior of political networks, both at the individual (party member) and ideological community levels. Our study relies on public data covering 15 years of voting sessions of the House of Representatives of two diverse party system, namely, Brazil and the United States. Whereas the former is an example of a highly fragmented party system, the latter illustrates the case of a highly polarized and non-fragmented system. We characterize the ideological communities, their member polarization and how such communities evolve over time. Also, we propose a temporal ideological space model, based on temporal vertice embeddings, which allows us to assess the individual changes in ideological behavior over time, as expressed by the party members' voting patterns. Â Our results reveal very distinct patterns across the two case studies, in terms of both structural and dynamic properties.
Copyright (c) 2019 Carlos Henrique Gomes Ferreira, Fabricio Murai Ferreira, Breno de Sousa Matos, Jussara Marques de Almeida
Fine-Tuning Gold Questions in Crowdsourcing Tasks using Probabilistic and Siamese Neural Network Models
The economic benefits of crowdsourcing have furthered its wide-spread use over the past decade. However, increasing numbers of fraudulent workers threaten to undermine the emerging crowdsourcing economy: requestors face the choice of either risk-ing low-quality results or having to pay extra money for quality safeguards such as gold questions or majority voting. The more safeguards injected into the workload, the lower are the risks im-posed by fraudulent workers, yet the higher are the costs. So, how many of them are actually needed? Is there a generally applicable number or percentage? This paper uses deep learning techniques to identify custom-tailored numbers of gold questions per worker for individually managing the cost/quality balance. Our new method follows real-life experiences: the more we know about workers before assigning a task, the clearer our belief or disbelief in this worker’s reliability gets. Employing probabilistic models, namely Bayesian belief networks and certainty factor models, our method creates worker profiles reflecting different a-priori belief values, and we prove that the actual number of gold questions per worker can indeed be assessed. Our evaluation on real-world crowdsourc-ing datasets demonstrates our method's efficiency in saving money while maintaining high-quality results.
Copyright (c) 2019 JOSE MARIA GONZALEZ PINTO, Kinda El Maarry, Wolf-Tilo Balke
Partisanship, Propaganda and Post-Truth Politics: Quantifying Impact in Online Debate
The recent past has highlighted the influential role of social networks and online media in shaping public debate on current affairs and political issues. This paper is focused on studying the role of politically-motivated actors and their strategies for influencing and manipulating public opinion online: partisan media, state-backed propaganda, and post-truth politics. In particular, we present quantitative research on the presence and impact of these three “Psâ€ in online Twitter debates in two contexts: (i) the run up to the UK EU membership referendum (“Brexitâ€); and (ii) the information operations of Russia-backed online troll accounts. We first compare the impact of highly partisan versus mainstream media during the Brexit referendum, specifically comparing tweets by half a million “leaveâ€ and “remainâ€ supporters. Next, online propaganda strategies are examined, specifically left- and right-wing troll accounts. Lastly, we study the impact of misleading claims made by the political leaders of the leave and remain campaigns. This is then compared to the impact of the Russia-backed partisan media and propaganda accounts during the referendum. In particular, just two of the many misleading claims made by politicians during the referendum were found to be cited in 4.6 times more tweets than the 7,103 tweets related to Russia Today and Sputnik and in 10.2 times more tweets than the 3,200 Brexit-related tweets by the Russian troll accounts.
Copyright (c) 2019 Genevieve Gorrell, Mehmet Emin Bakir, Ian Roberts, Mark Anthony Greenwood, Benedetta Iavarone, Kalina Bontcheva
Network, Text, and Image Analysis of Anti-Muslim Groups on Facebook
Islamophobic attitudes and overt acts of hostility toward Muslims in the United States are increasingly commonplace. The goal of this research is to begin to understand how anti-Muslim political groups use the Facebook social network to build their online communities and perpetuate their beliefs. We used the public Facebook Graph API to create a large dataset of 700,204 members of 1,870 Facebook groups spanning 10 different far-right ideologies during the time period June 2017 - March 2018. We first applied social network analysis techniques to discover which groups and ideologies shared members with anti-Muslim groups during this period. Our results show that the anti-Muslim Facebook network has unique characteristics when compared to other categories of far-right extremism. We then assessed 202 anti-Muslim Facebook group cover photos and descriptions for evidence of Islamophobic content. Results indicate that these anti-Muslim groups rely on a predictable collection of visual and linguistic cues to propagate negative stereotypes about Muslims, and that the vast majority of these groups rely heavily on symbols and language that portray Islam as a violent enemy which is deserving of violence and hostility in return. By understanding the important role Islamophobia plays in the hate ecosystem on Facebook, social media users and platform providers can be better prepared to confront and condemn anti-Muslim bias.
Crawling Complex Networks: An Experimental Evaluation of Data Collection Algorithms and Network Structural Properties
In recent years, researchers and data analysts have increasingly used online social network data to study human behavior. Before such study can begin, one must first obtain appropriate data. This process poses many challenges: e.g. a this platform may provide a public API for accessing data, but such APIs are often rate limited, restricting the amount of data that an individual collect in a given amount of time. Thus, in order for the data collector to efficiently collect data, she needs to make intelligent use of her limited API queries. The network science literature has proposed numerous network crawling methods, but it is not always easy for the data collector to select an appropriate method: methods that are successful on one network may fail on other networks. In this work, we demonstrate that the performance of network crawling methods is highly dependent on the structural properties of the network. To do that, we perform a detailed, hypothesis-driven analysis of the performance of eight popular crawling methods with respect to the task of maximizing node coverage. We perform experiments on both directed and undirected networks, under five different query response models: complete, paginated, partial, in-out, and out responses. We identify three important network properties: community separation, average community size, and average node degree. We begin by performing controlled experiments on synthetic networks, and then verify our observations on real networks. Finally, we provide guidelines to data collectors on how to select an appropriate crawling method for a particular network.
Copyright (c) 2019 Katchaguy Areekijseree, Sucheta Soundarajan
A Content-Linking-Context Model for Automatic Assessment of Web Resources in “Notice-and-take-downâ€ Procedures
The US Digital Millennium Copyright Act (DMCA) of 1998 adopted a notice-and-take-down procedure to help tackle alleged online infringements through online service providers’ actions. The European Directive 2000/31/EC (e-Commerce Directive) introduced similar liability exemptions, but did not specify any take-down procedure. Many intermediary (host, and online search engine) service providers even in Europe have followed this notice-and-take-down procedure to enable copyright owners to issue notices to take down allegedly infringing Web resources. However, the accuracy of take-down is not known, and notice receivers do not reveal clear information about how they check the legitimacy of these requests, about whether and how they check the lawfulness of allegedly infringing content, or what criteria they use for these actions. In this paper, we use Google’s Transparency Report as the benchmark to investigate the information content of take-down notices and the accuracy of the resulting take-downs of allegedly infringing Web resources. The analysis of copyright infringement is limited to the five scenarios most frequently encountered in our study of Web resources. Based on our investigation, we propose a Content-Linking-Context (CLC) model of the criteria to be considered by intermediary service providers to achieve more accurate take-down, and investigate technical issues applying the CLC Model to automatically assess web resources and output a ‘likelihood of infringement’ score.
Copyright (c) 2017 Pei Zhang, Sophie Stalla-Bourdillon, Lester Gilbert
Mapping the Topics and Intellectual Structure of Web Science
This paper describes a mixed methods analysis of papers published at the ACM Web Science Conference series from 2009 to 2016, using co-citation analysis, bibliographic coupling, natural language processing, topic modelling and network visualisation techniques. The knowledge base of the Web Science community and the knowledge transfer from the ACM Web Science Conference series are studied, revealing major themes and key authors as a map of Web Science. In particular, the foundations of the Web Science community are revealed via co-citation analysis of authors of papers cited by ACM Web Science papers, while NLP analysis reveals topical descriptors and application contexts of Web Science. Finally, author-based bibliographic coupling of papers published at Web Science reveals authors who have been influenced by the Web Science community. In sum, this paper presents a knowledge map of the Web Science discipline visualizing topical foci, methodical roots in various disciplines, and key players in Web Science research.
Copyright (c) 2019 Clare Hooper, Peters Isabella, Cécile Robin
Tracking onscreen gender and role bias over time
Film and television play an important role in popular culture. However studies that require watching and annotating video are time-consuming and expensive to run at scale. We explore information mined from media database cast lists to explore the evolution of different roles over time. We focus on the gender distribution of those roles and how this changes over time. We compare real-life em- ployment gender distributions to our web-mediated onscreen gender data. Finally, we investigate how gender role biases differ between film and television. We propose these methodologies are a useful adjunct to traditional analysis that allow researchers to explore the relationship between online and on- screen gender depictions.
Copyright (c) 2016 Will Radford, Matthias Gallé
Exploring the Relationship between User Activities and Profile Images on Twitter through Machine Learning Techniques
Social media profile images are one of many visual components of users. Moreover, user activities such as posting or chatting are regarded as self-expression behaviors. In this study, we examine Japanese Twitter users to explore the relationship between user activities and profile images. Logistic regression analysis is used to statistically identify and quantify relationships, leading us to conclude that several profile image categories significantly correlate with user activities. Furthermore, we use machine learning techniques (logistic regression, random forest, and support vector machine) to predict whether or not a user belongs to a specific profile image category. Each model's performance is evaluated and compared for all profile image categories. Primary results show that users whose profile image includes others' faces are more likely to use a replying function but less likely to add url links to their tweets, and that it is the easiest for machine learning models to find their category from their user activities. In short, our findings indicate that visual expression correlates with social media user behavior.
Copyright (c) 2017 Tomu Tominaga, Yoshinori Hijikata
Towards Understanding the Consumption of Video-Ads on YouTube
Being the most popular online video platform nowadays, YouTube is a complex ecosystem that generates billions of dollars of revenue yearly. This revenue mostly stems from online advertisements that are shown on the website. Like other social media platforms, YouTube enables any user to create and upload content, create ad-campaigns that promote advertisement content, as well as monetize channels (i.e., YouTube video uploaders) by showing ads from other channels to viewers. More importantly, any individual can watch videos for free and, in consequence, be exposed to advertisements. The mediation of these different parties that interact through ads, as well as the YouTube platform itself is done by online ad auction algorithms. In this paper, we study the aforementioned ecosystem through the use of advertisements in the form of video (video-ads). Online video-ads are a novel medium that is gaining significant traction on social media platforms like YouTube. Our study presents insights on (1) the behavior of users when exposed to video-ads; (2) the popularity of the video-ads over time; (3) the relation between contextual advertising and the effectiveness of ads; (4) the success of ads in generating revenue; and, (5) the success of channels in attracting revenue as exposers of ads. The results here presented have practical implications for content providers, creators, channels, and YouTube viewers.
Copyright (c) 2017 Mariana Arantes, Flavio Figueiredo, Jussara M. Almeida
Multi-Cultural Interlinking of Web Taxonomies with ACROSS
The Web hosts a huge variety of multi-cultural taxonomies. They encompass product catalogs of e-commerce, general-purpose knowledge bases and numerous domain-specific category systems. The enormous heterogeneity of those sources is a challenging aspect when multiple taxonomies have to be interlinked. In this paper we introduce ACROSS system to support the alignment of independently created Web taxonomies. For mapping categories across different taxonomies, ACROSS harnesses instance-level features as well as distant supervision from an intermediate source like multiple Wikipedia editions. ACROSS includes a reasoning step, which is based on combinatorial optimization. In order to reduce the run time of the reasoning procedure without sacrificing the quality, we study two models of user involvement. Our experiments with heterogeneous taxonomies for different domains demonstrate the viability of our approach and improvement over state-of-the-art baselines.
Copyright (c) 2017 Natalia Boldyrev, Marc Spaniol, Gerhard Weikum
Predicting Online Islamophopic Behavior after #ParisAttacks
The Paris terrorist attacks occurred on November 13, 2015, prompting a massive response on social media including Twitter, with millions of posted tweets in the first few hours after the attacks. Most of the tweets were condemning the attacks and showing support to Parisians. One of the trending debates related to the attacks concerned possible association between terrorism and Islam, and Muslims in general. This created a global discussion between those attacking and those defending Islam and Muslims. In this paper, we use this incident to examine the effect of online social network interactions prior to an event to predict what attitudes will be expressed in response to the event. Specifically, we focus on how a person's online content and network dynamics can be used to predict future attitudes and stance in the aftermath of a major event. In our study, we collected a set of 8.36 million tweets related to the Paris attacks within the 50 hours following the event, of which we identified over 900k tweets mentioning Islam and Muslims. We quantitatively analyzed users' network interactions and historical tweets to predict their attitudes towards Islam and Muslim. We provide a description of the quantitative results based on the content (hashtags) and network interactions (retweets, replies, and mentions). We analyze two types of data: (1) we use post-event tweets to learn users' stated stance towards Muslims based on sampling methods and crowd-sourced annotations; and (2) we employ pre-event interactions on Twitter to build a classifier to predict post-event stance. We found that pre-event network interactions can predict attitudes towards Muslims with 82\% macro F-measure, even in the absence of prior mentions of Islam, Muslims, or related terms.
Copyright (c) 2017 Kareem Darwish, Walid Magdy, Afshin Rahimi, Timothy Baldwin, Norah Abokhodair
On the Ubiquity of Web Tracking: Insights from a Billion-Page Web Crawl
We perform a large-scale analysis of third-party trackers on the World Wide Web. We extract third-party embeddings from more than 3.5~billion web pages of the CommonCrawl 2012 corpus, and aggregate those to a dataset containing more than 140 million third-party embeddings in over 41 million domains. To the best of our knowledge, this constitutes the largest empirical web tracking dataset collected so far, and exceeds related studies by more than an order of magnitude in the number of domains and web pages analyzed. Due to the enormous size of the dataset, we are able to perform a large-scale study of online tracking, on three levels: (1) On a global level, we give a precise figure for the extent of tracking, give insights into the structural properties of the `online tracking sphere' and analyse which trackers (and subsequently, which companies) are used by how many websites. (2) On a country-specific level, we analyse which trackers are used by websites in different countries, and identify the countries in which websites choose significantly different trackers than in the rest of the world. (3) We answer the question whether the content of websites influences the choice of trackers they use, leveraging more than ninety thousand categorized domains. In particular, we analyse whether highly privacy-critical websites about health and addiction make different choices of trackers than other websites. Based on the performed analyses, we confirm that trackers are widespread (as expected), and that a small number of trackers dominates the web (Google, Facebook and Twitter). In particular, the three tracking domains with the highest PageRank are all owned by Google. The only exception to this pattern are a few countries such as China and Russia. Our results suggest that this dominance is strongly associated with country-specific political factors such as freedom of the press. Furthermore, our data confirms that Google still operates services on Chinese websites, despite its proclaimed retreat from the Chinese market. We also confirm that websites with highly privacy-critical content are less likely to contain trackers (60\% vs 90\% for other websites), even though the majority of them still do contain trackers.
Copyright (c) 2018 Sebastian Schelter, Jérôme Kunegis
Identity Assurance in the UK: technical implementation and legal implications under the eIDAS Regulation
Gov.UK Verify, the new Electronic Identity Management (eIDM) system of the UK Government, has been promoted as a state-of-the-art privacy-preserving system, tailored to meet the requirements of UK citizens and is the first eIDM in which the government delegates the provision of identity to competing third parties. According to the recently enacted EU eIDAS Regulation, Member States can allow their citizens to transact with foreign services by notifying their national eID scheme. Once a scheme is notified, all other Member States are obligated to incorporate it into their electronic identication procedures. This article examines Gov.UK Verify's compliance with the requirements set forth by the Regulation and the impact on privacy and data protection. It then explores potential interoperability issues with other national eID schemes, using the German nPA, an eIDM based on national identity cards, as a reference point. It concludes with a general overview of legal equivalence of third country legal frameworks compared with that of eIDAS. The article contributes to relevant literature of privacy-preserving eID management by offering policy and technical recommendations for compliance with the new Regulation and an evaluation of interoperability under eIDAS between systems of different architecture. It is also, to our knowledge, the first exploration of the future of eID management in the UK after a potential exit from the European Union.
Copyright (c) 2017 Niko Tsakalakis, Sophie Stalla-Bourdillon, Kieron O'Hara
Spreading One's Tweets: How Can Journalists Gain Attention for their Tweeted News?
Traditional news media face many serious concerns as their distribution channels are gradually being taken over by third parties (e.g., bloggers, citizen journalists, and news aggregators). If traditional media is to remain competitive, it needs to develop innovative strategies around these channels, to maximize audience engagement with the news it provides. In this paper, we focus on the issue of developing one such strategy for spreading news on Twitter. Using tweet corpora from two national news ecosystems -- 1.7M tweets from 200 journalists in Ireland and 1.2M tweets from 364 journalists in the UK --Â and audience responses to these tweets, we develop predictive models to identify the features of journalists and news tweets that impact audience attention. These analyses reveal that different combinations of features influence audience engagement differentially from one news category to the next (e.g., sport versus business). Using these findings, we suggest a set of guidelines for journalists, designed to help them maximize engagement with the news they tweet. Finally, we discuss how such analyses can inform innovative dissemination strategies in digital media.
Copyright (c) 2017 Claudia Orellana-Rodriguez, Derek Greene, Mark T. Keane
It’s All About Information? The Following Behaviour of Professors and PhD Students in Computer Science on Twitter
In this paper we investigate the role of the academic status in the following behaviour of computer scientists on Twitter. Based on a uses and gratifications perspective, we focus on the activity of a Twitter account and the reciprocity of following relationships. We propose that the account activity addresses the users’ information motive only, whereas the user’s academic status relates to both the information motive and community development (as in peer networking or career planning). Variables were extracted from Twitter user data. We applied a biographical approach to correctly identify the academic status (professor versus PhD student). We calculated a 2 × 2 MANOVA on the influence of the activity of the account and the academic status (on different groups of followers) to differentiate the influence of the information motive versus the motive for community development. Results suggest that for computer scientists Twitter is mainly an information network. However, we found significant effects in the sense of career planning, that is, the accounts of professors had even in the case of low activity a relatively high number of researcher followers – both PhD followers as well as professor followers. Additionally, there was also some weak evidence for community development gratifications in the sense of peer-networking of professors. Overall, we conclude that the academic use of Twitter is not only about information, but also about career planning and networking.
Copyright (c) 2017 Stephanie Linek, Asmelash Teka Hadgu, Christian Pieter Hoffmann, Robert Jäschke, Cornelius Puschmann
Improving Collaborative Filtering Using a Cognitive Model of Human Category Learning
Classic resource recommenders like Collaborative Filtering treat users as being just another entity, thereby neglecting non-linear user-resource dynamics that shape attention and interpretation. SUSTAIN, as an unsupervised human category learning model, captures these dynamics. It aims to mimic a learner’s behavior of categorization. In this paper, we use three social bookmarking datasets gathered from BibSonomy, CiteULike and Delicious to investigate SUSTAIN as a user modeling approach to re-rank and enrich Collaborative Filtering following a hybrid recommender strategy. Evaluations against baseline algorithms in terms of recommender accuracy and computational complexity reveal encouraging results. Our approach substantially improves Collaborative Filtering and, depending on the dataset, successfully competes with a computationally much more expensive Matrix Factorization variant. In a further step, we explore SUSTAIN’s dynamics in our specific learning task and show that both, memorization of a user’s history and clus- tering, contribute to the algorithm’s performance. Finally, we observe that the users’ attentional foci determined by SUSTAIN correlate with the users’ level of curiosity, identified by the SPEAR algorithm. Overall, the results of our study show that SUSTAIN can be used to efficiently model attention-interpretation dynamics of users and can help to improve Collaborative Filtering in resource recommendation tasks.
Overlap in the Web Search Results of Google and Bing
Google and Bing have emerged as the diarchy that arbitrates what documents are seen by Web searchers, particularly those desiring English language documents. We seek to study how distinctive are the top results presented to the users by the two search engines. A recent eye-tracking has shown that the web searchers decide whether to look at a document primarily based on the snippet and secondarily on the title of the document on the web search result page, and rarely based on the URL of the document. Given that the snippet and title generated by different search engines for the same document are often syntactically different, we first develop tools appropriate for conducting this study. Our empirical evaluation using these tools shows a surprising agreement in the results produced by the two engines for a wide variety of queries used in our study. Thus, this study raises the open question whether it is feasible to design a search engine that would produce results distinct from those produced by Google and Bing that the users will find helpful.Â
Copyright (c) 2016 Rakesh Agrawal, Behzad Golshan, Evangelos Papalexakis
The Graph Structure in the Web -- Analyzed on Different Aggregation Levels
Knowledge about the general graph structure of the World Wide Web is important for understanding the social mechanisms that govern its growth, for designing ranking methods, for devising better crawling algorithms, and for creating accurate models of its structure. In this paper, we analyze a large web graph. The graph was extracted from a large publicly accessible web crawl that was gathered by the Common Crawl Foundation in 2012. The graph covers over 3:5 billion web pages and 128:7 billion links. We analyse and compare, among other features, degree distributions, connectivity, average distances, and the structure of weakly/strongly connected components. We conduct our analysis on three different levels of aggregation: page, host, and pay-level domain (PLD) (one “dot levelâ€ above public sufï¬xes). Our analysis shows that, as evidenced by previous research , some of the features previously observed by Broder et al. Â are very dependent on artifacts of the crawling process, whereas other appear to be more structural. We conï¬rm the existence of a giant strongly connected component; we however ï¬nd, as observed by other researchers, very different proportions of nodes that can reach or that can be reached from the giant component, suggesting that the “bow-tie structureâ€ as described in is strongly dependent on the crawling process, and to the best of our current knowledge is not a structural property of the web. More importantly, statistical testing and visual inspection of sizerank plots show that the distributions of indegree, outdegree and sizes of strongly connected components of the page and host graph are not power laws, contrarily to what was previously reported for much smaller crawls, although they might be heavy tailed. If we aggregate at pay-level domain, however, a power law emerges. We Â also provide for the ï¬rst time accurate measurement of distancebased features, using recently introduced algorithms that scale to the size of our crawl.
Copyright (c) 2015 Robert Meusel, Sebastiano Vigna, Oliver Lehmberg, Christian Bizer
The Wisdom of the Few? “Supertaggersâ€ in Collaborative Tagging Systems
A folksonomy is ostensibly an information structure built up by the “wisdom of the crowdâ€, but is the “crowdâ€ really doing the work? Tagging is in fact a sharply skewed process in which a small minority of “supertaggerâ€ users generate an overwhelming majority of the annotations. Using data from three large-scale social tagging platforms, we explore (a) how to best quantify the imbalance in tagging behavior and formally define a supertagger, (b) how supertaggers differ from other users in their tagging patterns, and (c) if effects of motivation and expertise inform our understanding of what makes a supertagger. Our results indicate that such prolific users not only tag more than their counterparts, but in quantifiably different ways. Specifically, we find that supertaggers are more likely to label content in the long tail of less popular items, that they show differences in patterns of content tagged and terms utilized, and are measurably different with respect to tagging expertise and motivation. These findings suggest we should question the extent to which folksonomies achieve crowdsourced classification via the “wisdom of the crowdâ€, especially for broad folksonomies like Last.fm as opposed to narrow folksonomies like Flickr.Â
Copyright (c) 2015 Jared Lorince, Sam Zorowitz, Jaimie Murdock, Peter M. Todd
An Extended Investigation of the Similarity Between Privacy Policies of Social Networking Sites as a Precursor for Standardization
Privacy policies are unsatisfactory in communicating information to users. Social networking sites (SNS) exemplify this, attracting growing concerns regarding their use of personal data, but lack incentives to improve their policies. Standardization addresses many of these issues, but is only possible if policies share attributes which can be standardized. This investigation assessed the similarity of two attributes (the clauses used and the coverage of forty recommendations made by the UK Information Commissioner) between the privacy policies of the six most frequently visited SNS globally. Similarity was also investigated by looking at whether there were any recommendations all SNS did not address and any themes of information discussed in the policies but not included in the ICO Code. Similarity in the clauses was low, yet similarity in the recommendations covered was high, indicating SNS use different clauses, but to convey similar information. There were a number of ICO Code recommendations which none of the SNS addressed and four themes of information that all six SNS addressed, which were not present in the ICO Code. This paper proposes the policies of SNS already share attributes, indicating the feasibility of standardization and five recommendations are made to begin facilitating this.
Copyright (c) 2016 Emma Cradock, David Millard, Sophie Stalla-Bourdillon