arXiv subjects

Gareth Tyson

Publications and source records attributed to Gareth Tyson.

At least 73 records · Page 4Linked to original sources

arXiv2020

A First Look at COVID-19 Messages on WhatsApp in Pakistan

The worldwide spread of COVID-19 has prompted extensive online discussions, creating an `infodemic' on social media platforms such as WhatsApp and Twitter. However, the information shared on these platforms is prone to be unreliable and/or misleading. In this paper, we present the first analysis of COVID-19 discourse on public WhatsApp groups from Pakistan. Building on a large scale annotation of thousands of messages containing text and images, we identify the main categories of discussion. We focus on COVID-19 messages and understand the different types of images/text messages being propagated. By exploring user behavior related to COVID messages, we inspect how misinformation is spread. Finally, by quantifying the flow of information across WhatsApp and Twitter, we show how information spreads across platforms and how WhatsApp acts as a source for much of the information shared on Twitter.

cs.SI↗

arXiv2020

Characterising and Detecting Sponsored Influencer Posts on Instagram

Recent years have seen a new form of advertisement campaigns emerge: those involving so-called social media influencers. These influencers accept money in return for promoting products via their social media feeds. Although this constitutes a new and interesting form of marketing, it also raises many questions, particularly related to transparency and regulation. For example, it can sometimes be unclear which accounts are officially influencers, or what even constitutes an influencer/advert. This is important in order to establish the integrity of influencers and to ensure compliance with advertisement regulation. We gather a large-scale Instagram dataset covering thousands of accounts advertising products, and create a categorisation based on the number of users they reach. We then provide a detailed analysis of the types of products being advertised by these accounts, their potential reach, and the engagement they receive from their followers. Based on our findings, we train machine learning models to distinguish sponsored content from non-sponsored, and identify cases where people are generating sponsored posts without officially labelling them. Our findings provide a first step towards understanding the under-studied space of online influencers that could be useful for researchers, marketers and policymakers.

cs.SI↗

arXiv2020

Impersonation on Social Media: A Deep Neural Approach to Identify Ingenuine Content

Impersonators are playing an important role in the production and propagation of the content on Online Social Networks, notably on Instagram. These entities are nefarious fake accounts that intend to disguise a legitimate account by making similar profiles and then striking social media by fake content, which makes it considerably harder to understand which posts are genuinely produced. In this study, we focus on three important communities with legitimate verified accounts. Among them, we identify a collection of 2.2K impersonator profiles with nearly 10k generated posts, 68K comments, and 90K likes. Then, based on profile characteristics and user behaviours, we cluster them into two collections of `bot' and `fan'. In order to separate the impersonator-generated post from genuine content, we propose a Deep Neural Network architecture that measures `profiles' and `posts' features to predict the content type: `bot-generated', 'fan-generated', or `genuine' content. Our study shed light into this interesting phenomena and provides interesting observation on bot-generated content that can help us to understand the role of impersonators in the production of fake content on Instagram.

cs.SI↗

arXiv2020

Analyzing Temporal Relationships between Trending Terms on Twitter and Urban Dictionary Activity

As an online, crowd-sourced, open English-language slang dictionary, the Urban Dictionary platform contains a wealth of opinions, jokes, and definitions of terms, phrases, acronyms, and more. However, it is unclear exactly how activity on this platform relates to larger conversations happening elsewhere on the web, such as discussions on larger, more popular social media platforms. In this research, we study the temporal activity trends on Urban Dictionary and provide the first analysis of how this activity relates to content being discussed on a major social network: Twitter. By collecting the whole of Urban Dictionary, as well as a large sample of tweets over seven years, we explore the connections between the words and phrases that are defined and searched for on Urban Dictionary and the content that is talked about on Twitter. Through a series of cross-correlation calculations, we identify cases in which Urban Dictionary activity closely reflects the larger conversation happening on Twitter. Then, we analyze the types of terms that have a stronger connection to discussions on Twitter, finding that Urban Dictionary activity that is positively correlated with Twitter is centered around terms related to memes, popular public figures, and offline events. Finally, We explore the relationship between periods of time when terms are trending on Twitter and the corresponding activity on Urban Dictionary, revealing that new definitions are more likely to be added to Urban Dictionary for terms that are currently trending on Twitter.

cs.CL↗

arXiv2020

A First Instagram Dataset on COVID-19

The novel coronavirus (COVID-19) pandemic outbreak is drastically shaping and reshaping many aspects of our life, with a huge impact on our social life. In this era of lockdown policies in most of the major cities around the world, we see a huge increase in people and professional engagement in social media. Social media is playing an important role in news propagation as well as keeping people in contact. At the same time, this source is both a blessing and a curse as the coronavirus infodemic has become a major concern, and is already a topic that needs special attention and further research. In this paper, we provide a multilingual coronavirus (COVID-19) Instagram dataset that we have been continuously collected since March 30, 2020. We are making our dataset available to the research community at Github. We believe that this contribution will help the community to better understand the dynamics behind this phenomenon in Instagram, as one of the major social media. This dataset could also help study the propagation of misinformation related to this outbreak.

cs.SI↗

arXiv2020

Characterising User Content on a Multi-lingual Social Network

Social media has been on the vanguard of political information diffusion in the 21st century. Most studies that look into disinformation, political influence and fake-news focus on mainstream social media platforms. This has inevitably made English an important factor in our current understanding of political activity on social media. As a result, there has only been a limited number of studies into a large portion of the world, including the largest, multilingual and multi-cultural democracy: India. In this paper we present our characterisation of a multilingual social network in India called ShareChat. We collect an exhaustive dataset across 72 weeks before and during the Indian general elections of 2019, across 14 languages. We investigate the cross lingual dynamics by clustering visually similar images together, and exploring how they move across language barriers. We find that Telugu, Malayalam, Tamil and Kannada languages tend to be dominant in soliciting political images (often referred to as memes), and posts from Hindi have the largest cross-lingual diffusion across ShareChat (as well as images containing text in English). In the case of images containing text that cross language barriers, we see that language translation is used to widen the accessibility. That said, we find cases where the same image is associated with very different text (and therefore meanings). This initial characterisation paves the way for more advanced pipelines to understand the dynamics of fake and political content in a multi-lingual and non-textual setting.

cs.SI↗

arXiv2020

Characterizing EOSIO Blockchain

EOSIO has become one of the most popular blockchain platforms since its mainnet launch in June 2018. In contrast to the traditional PoW-based systems (e.g., Bitcoin and Ethereum), which are limited by low throughput, EOSIO is the first high throughput Delegated Proof of Stake system that has been widely adopted by many applications. Although EOSIO has millions of accounts and billions of transactions, little is known about its ecosystem, especially related to security and fraud. In this paper, we perform a large-scale measurement study of the EOSIO blockchain and its associated DApps. We gather a large-scale dataset of EOSIO and characterize activities including money transfers, account creation and contract invocation. Using our insights, we then develop techniques to automatically detect bots and fraudulent activity. We discover thousands of bot accounts (over 30\% of the accounts in the platform) and a number of real-world attacks (301 attack accounts). By the time of our study, 80 attack accounts we identified have been confirmed by DApp teams, causing 828,824 EOS tokens losses (roughly 2.6 million US\$) in total.

cs.CR↗

arXiv2020

Dissecting the Workload of a Major Adult Video Portal

Adult content constitutes a major source of Internet traffic. As with many other platforms, these sites are incentivized to engage users and maintain them on the site. This engagement (e.g., through recommendations) shapes the journeys taken through such sites. Using data from a large content delivery network, we explore session journeys within an adult website. We take two perspectives. We first inspect the corpus available on these platforms. Following this, we investigate the session access patterns. We make a number of observations that could be exploited for optimizing delivery, e.g., that users often skip within video streams.

cs.CY↗

arXiv2019

An Empirical Study of the Cost of DNS-over-HTTPS

DNS is a vital component for almost every networked application. Originally it was designed as an unencrypted protocol, making user security a concern. DNS-over-HTTPS (DoH) is the latest proposal to make name resolution more secure. In this paper we study the current DNS-over-HTTPS ecosystem, especially the cost of the additional security. We start by surveying the current DoH landscape by assessing standard compliance and supported features of public DoH servers. We then compare different transports for secure DNS, to highlight the improvements DoH makes over its predecessor, DNS-over-TLS (DoT). These improvements explain in part the significantly larger take-up of DoH in comparison to DoT. Finally, we quantify the overhead incurred by the additional layers of the DoH transport and their impact on web page load times. We find that these overheads only have limited impact on page load times, suggesting that it is possible to obtain the improved security of DoH with only marginal performance impact.

cs.NI↗

arXiv2019

Challenges in the Decentralised Web: The Mastodon Case

The Decentralised Web (DW) has recently seen a renewed momentum, with a number of DW platforms like Mastodon, Peer-Tube, and Hubzilla gaining increasing traction. These offer alternatives to traditional social networks like Twitter, YouTube, and Facebook, by enabling the operation of web infrastructure and services without centralised ownership or control. Although their services differ greatly, modern DW platforms mostly rely on two key innovations: first, their open source software allows anybody to setup independent servers ("instances") that people can sign-up to and use within a local community; and second, they build on top of federation protocols so that instances can mesh together, in a peer-to-peer fashion, to offer a globally integrated platform. In this paper, we present a measurement-driven exploration of these two innovations, using a popular DW microblogging platform (Mastodon) as a case study. We focus on identifying key challenges that might disrupt continuing efforts to decentralise the web, and empirically highlight a number of properties that are creating natural pressures towards recentralisation. Finally, our measurements shed light on the behaviour of both administrators (i.e., people setting up instances) and regular users who sign-up to the platforms, also discussing a few techniques that may address some of the issues observed.

cs.NI↗

arXiv2019

Shaping the Internet: 10 Years of IXP Growth

Over the past decade, IXPs have been playing a key role in enabling interdomain connectivity. Their traffic volumes have grown dramatically and their physical presence has spread throughout the world. While the relevance of IXPs is undeniable, their long-term contribution to the shaping of the current Internet is not fully understood yet. In this paper, we look into the impact on Internet routes of the intense IXP growth over the last decade. We observe that while in general IXPs only have a small effect in path shortening, very large networks do enjoy a clear IXP-enabled path reduction. We also observe a diversion of the routes, away from the central Tier-1 ASes supported by IXPs. Interestingly, we also find that whereas IXP membership has grown, large and central ASes have steadily moved away from public IXP peerings, whereas smaller ones have embraced them. Despite all this changes, we find though that a clear hierarchy remains, with a small group of highly central networks

cs.NI↗

arXiv2019

Who Watches the Watchmen: Exploring Complaints on the Web

Under increasing scrutiny, many web companies now offer bespoke mechanisms allowing any third party to file complaints (e.g., requesting the de-listing of a URL from a search engine). While this self-regulation might be a valuable web governance tool, it places huge responsibility within the hands of these organisations that demands close examination. We present the first large-scale study of web complaints (over 1 billion URLs). We find a range of complainants, largely focused on copyright enforcement. Whereas the majority of organisations are occasional users of the complaint system, we find a number of bulk senders specialised in targeting specific types of domain. We identify a series of trends and patterns amongst both the domains and complainants. By inspecting the availability of the domains, we also observe that a sizeable portion go offline shortly after complaints are generated. This paper sheds critical light on how complaints are issued, who they pertain to and which domains go offline after complaints are issued.

cs.CY↗

arXiv2019

Pythia: a Framework for the Automated Analysis of Web Hosting Environments

A common approach when setting up a website is to utilize third party Web hosting and content delivery networks. Without taking this trend into account, any measurement study inspecting the deployment and operation of websites can be heavily skewed. Unfortunately, the research community lacks generalizable tools that can be used to identify how and where a given website is hosted. Instead, a number of ad hoc techniques have emerged, e.g., using Autonomous System databases, domain prefixes for CNAME records. In this work we propose Pythia, a novel lightweight approach for identifying Web content hosted on third-party infrastructures, including both traditional Web hosts and content delivery networks. Our framework identifies the organization to which a given Web page belongs, and it detects which Web servers are self-hosted and which ones leverage third-party services to provide contents. To test our framework we run it on 40,000 URLs and evaluate its accuracy, both by comparing the results with similar services and with a manually validated groundtruth. Our tool achieves an accuracy of 90% and detects that under 11% of popular domains are self-hosted. We publicly release our tool to allow other researchers to reproduce our findings, and to apply it to their own studies.

cs.NI↗

arXiv2019

A Bibliometric Analysis of Publications in Computer Networking Research

This study uses the article content and metadata of four important computer networking periodicals-IEEE Communications Surveys and Tutorials (COMST), IEEE/ACM Transactions on Networking (TON), ACM Special Interest Group on Data Communications (SIGCOMM), and IEEE International Conference on Computer Communications (INFOCOM)-obtained using ACM, IEEE Xplore, Scopus and CrossRef, for an 18-year period (2000-2017) to address important bibliometrics questions. All of the venues are prestigious, yet they publish quite different research. The first two of these periodicals (COMST and TON) are highly reputed journals of the fields while SIGCOMM and INFOCOM are considered top conferences of the field. SIGCOMM and INFOCOM publish new original research. TON has a similar genre and publishes new original research as well as the extended versions of different research published in the conferences such as SIGCOMM and INFOCOM, while COMST publishes surveys and reviews (which not only summarize previous works but highlight future research opportunities). In this study, we aim to track the co-evolution of trends in the COMST and TON journals and compare them to the publication trends in INFOCOM and SIGCOMM. Our analyses of the computer networking literature include: (a) metadata analysis; (b) content-based analysis; and (c) citation analysis. In addition, we identify the significant trends and the most influential authors, institutes and countries, based on the publication count as well as article citations. Through this study, we are proposing a methodology and framework for performing a comprehensive bibliometric analysis on computer networking research. To the best of our knowledge, no such study has been undertaken in computer networking until now.

cs.DL↗

arXiv2019

The Chain of Implicit Trust: An Analysis of the Web Third-party Resources Loading

The Web is a tangled mass of interconnected services, where websites import a range of external resources from various third-party domains. However, the latter can further load resources hosted on other domains. For each website, this creates a dependency chain underpinned by a form of implicit trust between the first-party and transitively connected third-parties. The chain can only be loosely controlled as first-party websites often have little, if any, visibility of where these resources are loaded from. This paper performs a large-scale study of dependency chains in the Web, to find that around 50% of first-party websites render content that they did not directly load. Although the majority (84.91%) of websites have short dependency chains (below 3 levels), we find websites with dependency chains exceeding 30. Using VirusTotal, we show that 1.2% of these third-parties are classified as suspicious --- although seemingly small, this limited set of suspicious third-parties have remarkable reach into the wider ecosystem. By running sandboxed experiments, we observe a range of activities with the majority of suspicious JavaScript downloading malware; worryingly, we find this propensity is greater among implicitly trusted JavaScripts.

cs.CR↗

arXiv2018

Trollslayer: Crowdsourcing and Characterization of Abusive Birds in Twitter

As of today, abuse is a pressing issue to participants and administrators of Online Social Networks (OSN). Abuse in Twitter can spawn from arguments generated for influencing outcomes of a political election, the use of bots to automatically spread misinformation, and generally speaking, activities that deny, disrupt, degrade or deceive other participants and, or the network. Given the difficulty in finding and accessing a large enough sample of abuse ground truth from the Twitter platform, we built and deployed a custom crawler that we use to judiciously collect a new dataset from the Twitter platform with the aim of characterizing the nature of abusive users, a.k.a abusive birds, in the wild. We provide a comprehensive set of features based on users' attributes, as well as social-graph metadata. The former includes metadata about the account itself, while the latter is computed from the social graph among the sender and the receiver of each message. Attribute-based features are useful to characterize user's accounts in OSN, while graph-based features can reveal the dynamics of information dissemination across the network. In particular, we derive the Jaccard index as a key feature to reveal the benign or malicious nature of directed messages in Twitter. To the best of our knowledge, we are the first to propose such a similarity metric to characterize abuse in Twitter.

cs.CY↗

arXiv2018

WhatsApp, Doc? A First Look at WhatsApp Public Group Data

In this dataset paper we describe our work on the collection and analysis of public WhatsApp group data. Our primary goal is to explore the feasibility of collecting and using WhatsApp data for social science research. We therefore present a generalisable data collection methodology, and a publicly available dataset for use by other researchers. To provide context, we perform statistical exploration to allow researchers to understand what public WhatsApp group data can be collected and how this data can be used. Given the widespread use of WhatsApp, our techniques to obtain public data and potential applications are important for the community.

cs.SI↗

arXiv2018

On Analyzing Self-Driving Networks: A Systems Thinking Approach

The networking field has recently started to incorporate artificial intelligence (AI), machine learning (ML), big data analytics combined with advances in networking (such as software-defined networks, network functions virtualization, and programmable data planes) in a bid to construct highly optimized self-driving and self-organizing networks. It is worth remembering that the modern Internet that interconnects millions of networks is a `complex adaptive social system', in which interventions not only cause effects but the effects have further knock-on effects (not all of which are desirable or anticipated). We believe that self-driving networks will likely raise new unanticipated challenges (particularly in the human-facing domains of ethics, privacy, and security). In this paper, we propose the use of insights and tools from the field of "systems thinking"---a rich discipline developing for more than half a century, which encompasses qualitative and quantitative nonlinear models of complex social systems---and highlight their relevance for studying the long-term effects of network architectural interventions, particularly for self-driving networks. We show that these tools complement existing simulation and modeling tools and provide new insights and capabilities. To the best of our knowledge, this is the first study that has considered the relevance of formal systems thinking tools for the analysis of self-driving networks.

cs.CY↗