Content moderation is a major challenge for online platforms. While user-driven blocking is a common tool, its dynamics are usually hidden as moderation data is private. Bluesky makes moderation actions public-by-design, providing an unprecedented opportunity to study a community-driven moderation ecosystem at scale. We leverage this transparency to (1) map the ecosystem of moderation blocking actions across 34 million users, including both individual blocks and the through blocklists, (2) identify the signals that correlate with blocking, and (3) measure the consequences of these actions. We demonstrate that community blocking is widespread, with a volume several orders of magnitude higher than official takedowns, and affects the visibility of more than 90 % of Bluesky content. The blocked accounts represent the most active, popular, toxic, and politically inclined users. However, different blocklists target different types of accounts and behaviors. Finally, blocking does not decrease the popularity and activity of the blocked users and has a limited effect on the social graph. By quantifying its dynamics and trade-offs, our study provides empirical grounding for designing future moderation systems that are transparent, pluralistic, and resistant to centralized control. Taken together, this study provides the first large-scale, quantitative analysis of a community-driven moderation ecosystem, demonstrating how individual and collective interventions influence user behavior.

Microblogging is a crucial mode of online communication. However, launching a new microblogging platform remains challenging, largely due to network effects. This has resulted in entrenched (and undesirable) dominance by established players, such as X/Twitter. To overcome these network effects, Bluesky, an emerging microblogging platform, introduced starter packs — curated lists of accounts that users can follow with a single click. We ask if starter packs have the potential to tackle the critical problem of social bootstrapping in new online social networks. We assess whether starter packs have indeed been helpful in supporting Bluesky growth. Our dataset includes 25.05 × 10⁶ users and 335.42 × 10³ starter packs with 1.73 × 10⁶ members, covering the entire lifecycle of Bluesky. We study the usage of these starter packs, their ability to drive network and activity growth, and their potential downsides. We also quantify the benefits of starter packs for members and creators on user visibility and activity while identifying potential challenges. By evaluating starter packs’ effectiveness and limitations, we contribute to the broader discourse on platform growth strategies and competitive innovation in the social media landscape.

This paper introduces Mastodoner, a command-line tool and Python library aimed at simplifying access to public data on Mastodon, a prominent player in the Fediverse --- a decentralized network of interconnected social media platforms. Mastodoner addresses the challenges posed by Mastodon's decentralized nature by providing a unified interface for data collection, instance discovery, and secure data sharing. Through examples and demonstrations, this paper illustrates Mastodoner's capabilities in facilitating researchers' access to and analysis of public Mastodon data, thus advancing research in decentralized social media analytics. The tool and documentation are available at: https://github.com/harisbinzia/mastodoner.

Understanding online behaviors, communities, and trends through social media analytics is becoming increasingly important. Recent changes in the accessibility of platforms like Twitter have made Mastodon a valuable alternative for researchers. In this tutorial, we will explore methods for collecting and analyzing public data from Mastodon, a decentralized micro-blogging social network. Participants will learn about the architecture of Mastodon, techniques and best practices for data collection, and various analytical methods to derive insights from the collected data. This session aims to equip researchers with the skills necessary to harness the potential of Mastodon data in computational social science and social data science research.

The recent development of decentralised and interoperable social networks (such as the "fediverse") creates new challenges for content moderators. This is because millions of posts generated on one server can easily "spread" to another, even if the recipient server has very different moderation policies. An obvious solution would be to leverage moderation tools to automatically tag (and filter) posts that contravene moderation policies, e.g. related to toxic speech. Recent work has exploited the conversational context of a post to improve this automatic tagging, e.g. using the replies to a post to help classify if it contains toxic speech. This has shown particular potential in environments with large training sets that contain complete conversations. This, however, creates challenges in a decentralised context, as a single conversation may be fragmented across multiple servers. Thus, each server only has a partial view of an entire conversation because conversations are often federated across servers in a non-synchronized fashion. To address this, we propose a decentralised conversation-aware content moderation approach suitable for the fediverse. Our approach employs a graph deep learning model (GraphNLI) trained locally on each server. The model exploits local data to train a model that combines post and conversational information captured through random walks to detect toxicity. We evaluate our approach with data from Pleroma, a major decentralised and interoperable micro-blogging network containing 2 million conversations. Our model effectively detects toxicity on larger instances, exclusively trained using their local post information (0.8837 macro-F1). Yet, we show that this approach does not perform well on smaller instances that do not possess sufficient local training data. Thus, in cases where a server contains insufficient data, we strategically retrieve information (posts or model parameters) from other servers to reconstruct larger conversations and improve results. With this, we show that we can attain a macro-F1 of 0.8826. Our approach has considerable scope to improve moderation in decentralised and interoperable social networks such as Pleroma or Mastodon.

An important concept in organisational behaviour is how hierarchy affects the voice of individuals, whereby members of a given organisation exhibit differing power relations based on their hierarchical position. Although there have been prior studies of the relationship between hierarchy and voice, they tend to focus on more qualitative small-scale methods and do not account for structural aspects of the organisation. This paper develops large-scale computational techniques utilising temporal network analysis to measure the effect that organisational hierarchy has on communication patterns throughout an organisation, focusing on the structure of pairwise interactions between individuals. To this end, we focus on one major organisation as a case study --- the Internet Engineering Task Force (IETF) --- a major technical standards development organisation for the Internet. A particularly useful feature of the IETF is a transparent hierarchy, where participants take on explicit roles (e.g., Area Directors, Working Group Chairs), and because its processes are open we have visibility into the communication of people at different hierarchy levels over a long time period. Exploiting this, we utilise a temporal network dataset of 989,911 email interactions among 23,741 participants to study how hierarchy impacts communication patterns. We show that the middle levels of the IETF are growing in terms of their dominance in communications. Higher levels consistently experience a higher proportion of incoming communication than lower levels, with higher levels initiating more communications too. We find that, overall, communication tends to flow "up" the hierarchy more than "down". Finally, we find that communication with higher-levels is associated with future communication more than for lower-levels, which we interpret as "facilitation". We conclude by discussing the implications this has on patterns within the wider IETF and the impact our analysis can have for other organisations.

Online comments within news articles are a key way people share opinions. Discovering insightful comments can, however, be challenging for readers. A solution to this problem is using comment curation, whereby professional editors select the highest quality comments manually --- referred to as ''editor-picks''. This paper studies the growing use of professional editor-curation for user-generated comments. We focus on the New York Times as a case study, using a dataset covering 80k articles. We study the characteristics of editor-pick comments, highlighting how editor criteria vary across news sections (e.g. sports, entertainment). We find that editor-pick comments tend to be longer, more relevant to the article, positive in sentiment, and contain low toxicity. Our analysis further reveals that editors within different news sections exhibit differing criteria when they perform comment selection. Thus, we finally propose a set of models that can automatically identify good candidate editor-picks. Our ultimate goal is to reduce editor and journalistic workload, increasing productivity and the quality of curated comments.

From health to education, income impacts a huge range of life choices. Earlier research has leveraged data from online social networks to study precisely this impact. In this paper, we ask the opposite question: do different levels of income result in different online behaviors? We demonstrate it does. We present the first large-scale study of Nextdoor, a popular location-based social network. We collect 2.6 Million posts from 64,283 neighborhoods in the United States and 3,325 neighborhoods in the United Kingdom, to examine whether online discourse reflects the income and income inequality of a neighborhood. We show that posts from neighborhoods with different incomes indeed differ, e.g. richer neighborhoods have a more positive sentiment and discuss crimes more, even though their actual crime rates are much lower. We then show that user-generated content can predict both income and inequality. We train multiple machine learning models and predict both income (R2=0.841) and inequality (R2=0.77).

As an alternative to Twitter and other centralized social networks, the Fediverse is growing in popularity. The recent, and polemical, takeover of Twitter by Elon Musk has exacerbated this trend. The Fediverse includes a growing number of decentralized social networks, such as Pleroma or Mastodon, that share the same subscription protocol (ActivityPub). Each of these decentralized social networks is composed of independent instances that are run by different administrators. Users, however, can interact with other users across the Fediverse regardless of the instance they are signed up to. The growing user base of the Fediverse creates key challenges for the administrators, who may experience a growing burden. In this paper, we explore how large that overhead is, and whether there are solutions to alleviate the burden. We study the overhead of moderation on the administrators. We observe a diversity of administrator strategies, with evidence that administrators on larger instances struggle to find sufficient resources. We then propose a tool, WatchGen, to semi-automate the process.

Adult video-sharing has undergone dramatic shifts. New platforms that directly interconnect (often amateur) producers and consumers now allow content creators to promote material across the web and directly monetize the content they produce. OnlyFans is the most prominent example of this new trend. OnlyFans is a content subscription service where creators earn money from users who subscribe to their material. In contrast to prior adult platforms, OnlyFans emphasizes creator-consumer interaction for audience accumulation and maintenance. This results in a wide cross-platform ecosystem geared towards bringing consumers to creators’ accounts. In this paper, we inspect this emerging ecosystem, focusing on content creators and the third-party platforms they connect to.

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.

Domain squatting, the adversarial tactic where attackers register domain names that mimic popular ones, has been observed for decades. However, there has been growing anecdotal evidence that this style of attack has spread to other domains. In this paper, we explore the presence of squatting attacks in the mobile app ecosystem. In “App Squatting”, attackers release apps with identifiers (e.g., app name or package name) that are confusingly similar to those of popular apps or well-known Internet brands. This paper presents the first in-depth measurement study of app squatting showing its prevalence and implications. We first identify 11 common deformation approaches of app squatters and propose “AppCrazy”, a tool for automatically generating variations of app identifiers. We have applied AppCrazy to the top-500 most popular apps in Google Play, generating 224,322 deformation keywords which we then use to test for app squatters on popular markets. Through this, we confirm the scale of the problem, identifying 10,553 squatting apps (an average of over 20 squatting apps for each legitimate one). Our investigation reveals that more than 51% of the squatting apps are malicious, with some being extremely popular (up to 10 million downloads). Meanwhile, we also find that mobile app markets have not been successful in identifying and eliminating squatting apps. Our findings demonstrate the urgency to identify and prevent app squatting abuses. To this end, we have publicly released all the identified squatting apps, as well as our tool AppCrazy.

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.

Headers are a critical part of HTTP, and it has been shown that they are increasingly subject to middlebox manipulation. Although this is well known, little is understood about the general regional and network trends that underpin these manipulations. In this paper, we collect data on thousands of networks to understand how they intercept HTTP headers in-the-wild. Our analysis reveals that 25% of measured ASes modify HTTP headers. Beyond this, we witness distinct trends among different regions and AS types; e.g., we observe high numbers of cache headers in poorly connected regions. Finally, we perform an in-depth analysis of the types of manipulations and how they differ across regions.