@book{abbateInventingInternet2000,
  title = {Inventing the {{Internet}}},
  author = {Abbate, Janet},
  year = {2000},
  series = {Inside Technology},
  edition = {3rd printing},
  publisher = {MIT Press},
  address = {Cambridge, Mass.},
  isbn = {978-0-262-51115-5},
  langid = {english}
}

@misc{AmericansWidelyDistrust2021,
  title = {Americans Widely Distrust {{Facebook}}, {{TikTok}} and {{Instagram}} with Their Data, Poll Finds},
  year = {2021},
  month = dec,
  journal = {Washington Post},
  urldate = {2024-03-09},
  abstract = {Pulled between not trusting some tech companies and still wanting to use their products, people look to government regulation.},
  chapter = {Technology},
  howpublished = {https://www.washingtonpost.com/technology/2021/12/22/tech-trust-survey/},
  langid = {english}
}

@article{baranDistributedCommunicationsNetworks1964,
  title = {On {{Distributed Communications Networks}}},
  author = {Baran, P.},
  year = {1964},
  month = mar,
  journal = {IEEE Transactions on Communications Systems},
  volume = {12},
  number = {1},
  pages = {1--9},
  issn = {1558-2647},
  doi = {10.1109/TCOM.1964.1088883},
  abstract = {This paper briefly reviews the distributed communication network concept in which each station is connected to all adjacent stations rather than to a few switching points, as in a centralized system. The payoff for a distributed configuration in terms of survivability in the cases of enemy attack directed against nodes, links or combinations of nodes and links is demonstrated. A comparison is made between diversity of assignment and perfect switching in distributed networks, and the feasibility of using low-cost unreliable communication links, even links so unreliable as to be unusable in present type networks, to form highly reliable networks is discussed. The requirements for a future all-digital data distributed network which provides common user service for a wide range of users having different requirements is considered. The use of a standard format message block permits building relatively simple switching mechanisms using an adaptive store-and-forward routing policy to handle all forms of digital data including digital voice. This network rapidly responds to changes in the network status. Recent history of measured network traffic is used to modify path selection. Simulation results are shown to indicate that highly efficient routing can be performed by local control without the necessity for any central, and therefore vulnerable, control point.},
  keywords = {Buildings,Centralized control,Communication networks,Communication switching,Communication system control,History,Information systems,Network synthesis,Routing,Telecommunication network reliability}
}

@inproceedings{burkeFeedMeMotivating2009,
  title = {Feed {{Me}}: {{Motivating Newcomer Contribution}} in {{Social Network Sites}}},
  shorttitle = {Feed {{Me}}},
  booktitle = {Proceedings of the {{SIGCHI Conference}} on {{Human Factors}} in {{Computing Systems}}},
  author = {Burke, Moira and Marlow, Cameron and Lento, Thomas},
  year = {2009},
  series = {{{CHI}} '09},
  pages = {945--954},
  publisher = {ACM},
  address = {New York, NY, USA},
  doi = {10.1145/1518701.1518847},
  urldate = {2017-08-02},
  abstract = {Social networking sites (SNS) are only as good as the content their users share. Therefore, designers of SNS seek to improve the overall user experience by encouraging members to contribute more content. However, user motivations for contribution in SNS are not well understood. This is particularly true for newcomers, who may not recognize the value of contribution. Using server log data from approximately 140,000 newcomers in Facebook, we predict long-term sharing based on the experiences the newcomers have in their first two weeks. We test four mechanisms: social learning, singling out, feedback, and distribution. In particular, we find support for social learning: newcomers who see their friends contributing go on to share more content themselves. For newcomers who are initially inclined to contribute, receiving feedback and having a wide audience are also predictors of increased sharing. On the other hand, singling out appears to affect only those newcomers who are not initially inclined to share. The paper concludes with design implications for motivating newcomer sharing in online communities.},
  isbn = {978-1-60558-246-7}
}

@article{bushWeMayThink1945,
  title = {As {{We May Think}}},
  author = {Bush, Vannevar},
  year = {1945},
  month = jul,
  journal = {The Atlantic},
  volume = {176},
  number = {1},
  pages = {101--108},
  urldate = {2020-03-04},
  abstract = {``Consider a future device {\dots}~~in which an individual stores all his books, records, and communications, and which is mechanized so that it may be consulted with exceeding speed and flexibility. It is an enlarged intimate supplement to his memory.''},
  langid = {american}
}

@inproceedings{cosleySuggestBotUsingIntelligent2007,
  title = {{{SuggestBot}}: {{Using Intelligent Task Routing}} to {{Help People Find Work}} in {{Wikipedia}}},
  shorttitle = {{{SuggestBot}}},
  booktitle = {Proceedings of the 12th {{International Conference}} on {{Intelligent User Interfaces}}},
  author = {Cosley, Dan and Frankowski, Dan and Terveen, Loren and Riedl, John},
  year = {2007},
  series = {{{IUI}} '07},
  pages = {32--41},
  publisher = {ACM},
  address = {New York, NY, USA},
  doi = {10.1145/1216295.1216309},
  urldate = {2016-05-23},
  abstract = {Member-maintained communities ask their users to perform tasks the community needs. From Slashdot, to IMDb, to Wikipedia, groups with diverse interests create community-maintained artifacts of lasting value (CALV) that support the group's main purpose and provide value to others. Said communities don't help members find work to do, or do so without regard to individual preferences, such as Slashdot assigning meta-moderation randomly. Yet social science theory suggests that reducing the cost and increasing the personal value of contribution would motivate members to participate more.We present SuggestBot, software that performs intelligent task routing (matching people with tasks) in Wikipedia. SuggestBot uses broadly applicable strategies of text analysis, collaborative filtering, and hyperlink following to recommend tasks. SuggestBot's intelligent task routing increases the number of edits by roughly four times compared to suggesting random articles. Our contributions are: 1) demonstrating the value of intelligent task routing in a real deployment; 2) showing how to do intelligent task routing; and 3) sharing our experience of deploying a tool in Wikipedia, which offered both challenges and opportunities for research.},
  isbn = {978-1-59593-481-9}
}

@misc{diazUsingMastodonWay2022,
  title = {Using {{Mastodon}} Is Way Too Complicated to Ever Topple {{Twitter}}},
  author = {Diaz, Jesus},
  year = {2022},
  month = nov,
  journal = {Fast Company},
  urldate = {2024-02-22},
  abstract = {Great idea in theory, a total pain in practice.},
  howpublished = {https://www.fastcompany.com/90808984/using-mastodon-is-way-too-complicated-to-ever-topple-twitter},
  langid = {english}
}

@book{driscollModemWorldPrehistory2022,
  title = {The Modem World: {{A}} Prehistory of Social Media},
  shorttitle = {The Modem World},
  author = {Driscoll, Kevin},
  year = {2022},
  month = apr,
  publisher = {Yale University Press},
  abstract = {The untold story about how the internet became social, and why this matters for its future``Whether you're reading this for a nostalgic romp or to understand the dawn of the internet, The Modem World will delight you with tales of BBS culture and shed light on how the decisions of the past shape our current networked world.''---danah boyd, author of It's Complicated: The Social Lives of Networked TeensFifteen years before the commercialization of the internet, millions of amateurs across North America created more than 100,000 small-scale computer networks. The people who built and maintained these dial-up bulletin board systems (BBSs) in the 1980s laid the groundwork for millions of others who would bring their lives online in the 1990s and beyond. From ham radio operators to HIV/AIDS activists, these modem enthusiasts developed novel forms of community moderation, governance, and commercialization. The Modem World tells an alternative origin story for social media, centered not in the office parks of Silicon Valley or the meeting rooms of military contractors, but rather on the online communities of hobbyists, activists, and entrepreneurs. Over time, countless social media platforms have appropriated the social and technical innovations of the BBS community. How can these untold stories from the internet's past inspire more inclusive visions of its future?},
  isbn = {978-0-300-26512-5},
  langid = {english},
  keywords = {Computers / History,Computers / Internet / General,History / Modern / 20th Century / General}
}

@misc{driscollWeMisrememberEternal2023,
  title = {Do We Misremember {{Eternal September}}?},
  shorttitle = {Do We Misremember {{Eternal September}}?},
  author = {Driscoll, Kevin},
  year = {2023},
  month = apr,
  journal = {FLOW},
  urldate = {2024-02-22},
  langid = {american}
}

@article{ekstrandCollaborativeFilteringRecommender2011,
  title = {Collaborative {{Filtering Recommender Systems}}},
  author = {Ekstrand, Michael D. and Riedl, John T. and Konstan, Joseph A.},
  year = {2011},
  month = may,
  journal = {Foundations and Trends{\textregistered} in Human--Computer Interaction},
  volume = {4},
  number = {2},
  pages = {81--173},
  publisher = {Now Publishers, Inc.},
  issn = {1551-3955, 1551-3963},
  doi = {10.1561/1100000009},
  urldate = {2024-05-21},
  abstract = {Collaborative Filtering Recommender Systems},
  langid = {english}
}

@article{fieslerMovingLandsOnline2020,
  title = {Moving across Lands: Online Platform Migration in Fandom Communities},
  shorttitle = {Moving across Lands},
  author = {Fiesler, Casey and Dym, Brianna},
  year = {2020},
  month = may,
  journal = {Proc. ACM Hum.-Comput. Interact},
  volume = {4},
  number = {CSCW1},
  pages = {042:1--042:25},
  doi = {10.1145/3392847},
  urldate = {2020-06-27},
  abstract = {When online platforms rise and fall, sometimes communities fade away, and sometimes they pack their bags and relocate to a new home. To explore the causes and effects of online community migration, we examine transformative fandom, a longstanding, technology-agnostic community surrounding the creation, sharing, and discussion of creative works based on existing media. For over three decades, community members have left and joined many different online spaces, from Usenet to Tumblr to platforms of their own design. Through analysis of 28 in-depth interviews and 1,886 survey responses from fandom participants, we traced these migrations, the reasons behind them, and their impact on the community. Our findings highlight catalysts for migration that provide insights into factors that contribute to success and failure of platforms, including issues surrounding policy, design, and community. Further insights into the disruptive consequences of migrations (such as social fragmentation and lost content) suggest ways that platforms might both support commitment and better support migration when it occurs.}
}

@article{freelonComputationalResearchPostAPI2018,
  title = {Computational {{Research}} in the {{Post-API Age}}},
  author = {Freelon, Deen},
  year = {2018},
  month = oct,
  journal = {Political Communication},
  volume = {35},
  number = {4},
  pages = {665--668},
  publisher = {Routledge},
  issn = {1058-4609},
  doi = {10.1080/10584609.2018.1477506},
  urldate = {2022-04-21},
  keywords = {API,computational,Facebook,social media,Twitter}
}

@article{gillespieContentModerationAI2020,
  title = {Content Moderation, {{AI}}, and the Question of Scale},
  author = {Gillespie, Tarleton},
  year = {2020},
  month = jul,
  journal = {Big Data \& Society},
  volume = {7},
  number = {2},
  pages = {2053951720943234},
  publisher = {SAGE Publications Ltd},
  issn = {2053-9517},
  doi = {10.1177/2053951720943234},
  urldate = {2021-09-28},
  abstract = {AI seems like the perfect response to the growing challenges of content moderation on social media platforms: the immense scale of the data, the relentlessness of the violations, and the need for human judgments without wanting humans to have to make them. The push toward automated content moderation is often justified as a necessary response to the scale: the enormity of social media platforms like Facebook and YouTube stands as the reason why AI approaches are desirable, even inevitable. But even if we could effectively automate content moderation, it is not clear that we should.},
  langid = {english},
  keywords = {Artificial intelligence,bias,content moderation,platforms,scale,social media}
}

@inproceedings{heFlockingMastodonTracking2023,
  title = {Flocking to {{Mastodon}}: {{Tracking}} the {{Great Twitter Migration}}},
  shorttitle = {Flocking to {{Mastodon}}},
  booktitle = {Proceedings of the 2023 {{ACM}} on {{Internet Measurement Conference}}},
  author = {He, Jiahui and Zia, Haris Bin and Castro, Ignacio and Raman, Aravindh and Sastry, Nishanth and Tyson, Gareth},
  year = {2023},
  month = oct,
  series = {{{IMC}} '23},
  pages = {111--123},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  doi = {10.1145/3618257.3624819},
  urldate = {2024-02-22},
  abstract = {The acquisition of Twitter by Elon Musk has spurred controversy and uncertainty among Twitter users. The move raised both praise and concerns, particularly regarding Musk's views on free speech. As a result, a large number of Twitter users have looked for alternatives to Twitter. Mastodon, a decentralized micro-blogging social network, has attracted the attention of many users and the general media. In this paper, we analyze the migration of 136,009 users from Twitter to Mastodon. We inspect the impact that this has on the wider Mastodon ecosystem, particularly in terms of user-driven pressure towards centralization. We further explore factors that influence users to migrate, highlighting the effect of users' social networks. Finally, we inspect the behavior of individual users, showing how they utilize both Twitter and Mastodon in parallel. We find a clear difference in the topics discussed on the two platforms. This leads us to build classifiers to explore if migration is predictable. Through feature analysis, we find that the content of tweets as well as the number of URLs, the number of likes, and the length of tweets are effective metrics for the prediction of user migration.},
  isbn = {9798400703829},
  keywords = {machine learning,mastodon,topic modeling,twitter,user migration}
}

@article{herlockerEvaluatingCollaborativeFiltering2004,
  title = {Evaluating Collaborative Filtering Recommender Systems},
  author = {Herlocker, Jonathan L. and Konstan, Joseph A. and Terveen, Loren G. and Riedl, John T.},
  year = {2004},
  month = jan,
  journal = {ACM Transactions on Information Systems},
  volume = {22},
  number = {1},
  pages = {5--53},
  issn = {1046-8188},
  doi = {10.1145/963770.963772},
  urldate = {2020-08-06},
  abstract = {Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.},
  keywords = {Collaborative filtering,evaluation,metrics,recommender systems}
}

@inproceedings{herlockerExplainingCollaborativeFiltering2000,
  title = {Explaining Collaborative Filtering Recommendations},
  booktitle = {Proceedings of the 2000 {{ACM}} Conference on {{Computer}} Supported Cooperative Work},
  author = {Herlocker, Jonathan L. and Konstan, Joseph A. and Riedl, John},
  year = {2000},
  month = dec,
  series = {{{CSCW}} '00},
  pages = {241--250},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  doi = {10.1145/358916.358995},
  urldate = {2020-08-05},
  abstract = {Automated collaborative filtering (ACF) systems predict a person's affinity for items or information by connecting that person's recorded interests with the recorded interests of a community of people and sharing ratings between like-minded persons. However, current recommender systems are black boxes, providing no transparency into the working of the recommendation. Explanations provide that transparency, exposing the reasoning and data behind a recommendation. In this paper, we address explanation interfaces for ACF systems - how they should be implemented and why they should be implemented. To explore how, we present a model for explanations based on the user's conceptual model of the recommendation process. We then present experimental results demonstrating what components of an explanation are the most compelling. To address why, we present experimental evidence that shows that providing explanations can improve the acceptance of ACF systems. We also describe some initial explorations into measuring how explanations can improve the filtering performance of users.},
  isbn = {978-1-58113-222-9},
  keywords = {collaborative filtering,explanations,GroupLens,MoviesLens,recommender systems}
}

@article{hooverMastodonBumpNow2023,
  title = {The {{Mastodon Bump Is Now}} a {{Slump}}},
  author = {Hoover, Amanda},
  year = {2023},
  month = feb,
  journal = {Wired},
  issn = {1059-1028},
  urldate = {2023-10-21},
  abstract = {Active users have fallen by more than 1 million since the exodus from Elon Musk's Twitter, suggesting the decentralized platform is not a direct replacement.},
  chapter = {tags},
  langid = {american},
  keywords = {communities,content moderation,elon musk,mastodon,platforms,social,social media,twitter}
}

@inproceedings{kieneSurvivingEternalSeptember2016,
  title = {Surviving an ``{{Eternal September}}'': {{How}} an Online Community Managed a Surge of Newcomers},
  shorttitle = {Surviving an "{{Eternal September}}"},
  booktitle = {Proceedings of the 2016 {{CHI Conference}} on {{Human Factors}} in {{Computing Systems}}},
  author = {Kiene, Charles and {Monroy-Hern{\'a}ndez}, Andr{\'e}s and Hill, Benjamin Mako},
  year = {2016},
  pages = {1152--1156},
  publisher = {ACM},
  address = {New York, NY},
  doi = {10.1145/2858036.2858356},
  urldate = {2016-07-05},
  abstract = {We present a qualitative analysis of interviews with participants in the NoSleep community within Reddit where millions of fans and writers of horror fiction congregate. We explore how the community handled a massive, sudden, and sustained increase in new members. Although existing theory and stories like Usenet's infamous "Eternal September" suggest that large influxes of newcomers can hurt online communities, our interviews suggest that NoSleep survived without major incident. We propose that three features of NoSleep allowed it to manage the rapid influx of newcomers gracefully: (1) an active and well-coordinated group of administrators, (2) a shared sense of community which facilitated community moderation, and (3) technological systems that mitigated norm violations. We also point to several important trade-offs and limitations.},
  isbn = {978-1-4503-3362-7},
  keywords = {newcomers,norms and governance,online communities,peer production,qualitative methods}
}

@misc{kingMastodonMe2024,
  title = {Mastodon {{Near Me}}},
  author = {King, Jaz-Michael},
  year = {2024},
  journal = {jaz-michael king},
  urldate = {2024-03-04},
  abstract = {A map and data directory showcasing ActivityPub service providers, each specifically catering to a certain locality or offering support in a notable language.},
  langid = {english}
}

@incollection{korenAdvancesCollaborativeFiltering2022,
  title = {Advances in {{Collaborative Filtering}}},
  booktitle = {Recommender Systems Handbook},
  author = {Koren, Yehuda and Rendle, Steffen and Bell, Robert},
  editor = {Ricci, Francesco and Ro{\d k}a{\d h}, Liʾor and Shapira, Bracha},
  year = {2022},
  edition = {Third edition},
  pages = {91--142},
  publisher = {Springer},
  address = {New York, NY},
  isbn = {978-1-07-162196-7 978-1-07-162199-8},
  langid = {english}
}

@misc{krasnoffMastodon101How2022,
  title = {Mastodon 101: How to Follow (and Unfollow) Other Accounts},
  shorttitle = {Mastodon 101},
  author = {Krasnoff, Barbara},
  year = {2022},
  month = dec,
  journal = {The Verge},
  urldate = {2024-03-04},
  abstract = {How to get started in Mastodon by following other people},
  howpublished = {https://www.theverge.com/23519279/mastodon-instance-follow-friend},
  langid = {english}
}

@book{krautBuildingSuccessfulOnline2011,
  ids = {kraut_building_2011,kraut_building_2011-1,kraut_building_2011-3},
  title = {Building {{Successful Online Communities}}: {{Evidence-Based Social Design}}},
  shorttitle = {Building {{Successful Online Communities}}},
  author = {Kraut, Robert E. and Resnick, Paul and Kiesler, Sara},
  year = {2011},
  publisher = {MIT Press},
  address = {Cambridge, Mass},
  isbn = {978-0-262-01657-5},
  lccn = {HM742 .K73 2011},
  keywords = {Computer networks,internet,Online social networks,Planning,Social aspects,Social aspects Planning,Social psychology}
}

@article{lacavaDriversSocialInfluence2023,
  title = {Drivers of Social Influence in the {{Twitter}} Migration to {{Mastodon}}},
  author = {La Cava, Lucio and Aiello, Luca Maria and Tagarelli, Andrea},
  year = {2023},
  month = dec,
  journal = {Scientific Reports},
  volume = {13},
  number = {1},
  pages = {21626},
  issn = {2045-2322},
  doi = {10.1038/s41598-023-48200-7},
  urldate = {2024-02-02},
  abstract = {The migration of Twitter users to Mastodon following Elon Musk's acquisition presents a unique opportunity to study collective behavior and gain insights into the drivers of coordinated behavior in online media. We analyzed the social network and the public conversations of about 75,000 migrated users and observed that the temporal trace of their migrations is compatible with a phenomenon of social influence, as described by a compartmental epidemic model of information diffusion. Drawing from prior research on behavioral change, we delved into the factors that account for variations of the effectiveness of the influence process across different Twitter communities. Communities in which the influence process unfolded more rapidly exhibit lower density of social connections, higher levels of signaled commitment to migrating, and more emphasis on shared identity and exchange of factual knowledge in the community discussion. These factors account collectively for 57\% of the variance in the observed data. Our results highlight the joint importance of network structure, commitment, and psycho-linguistic aspects of social interactions in characterizing grassroots collective action, and contribute to deepen our understanding of the mechanisms that drive processes of behavior change of online groups.},
  langid = {english}
}

@inproceedings{lamAddressingColdstartProblem2008,
  title = {Addressing Cold-Start Problem in Recommendation Systems},
  booktitle = {Proceedings of the 2nd International Conference on {{Ubiquitous}} Information Management and Communication},
  author = {Lam, Xuan Nhat and Vu, Thuc and Le, Trong Duc and Duong, Anh Duc},
  year = {2008},
  month = jan,
  series = {{{ICUIMC}} '08},
  pages = {208--211},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  doi = {10.1145/1352793.1352837},
  urldate = {2024-05-21},
  abstract = {Recommender systems for automatically suggested items of interest to users have become increasingly essential in fields where mass personalization is highly valued. The popular core techniques of such systems are collaborative filtering, content-based filtering and combinations of these. In this paper, we discuss hybrid approaches, using collaborative and also content data to address cold-start - that is, giving recommendations to novel users who have no preference on any items, or recommending items that no user of the community has seen yet. While there have been lots of studies on solving the item-side problems, solution for user-side problems has not been seen public. So we develop a hybrid model based on the analysis of two probabilistic aspect models using pure collaborative filtering to combine with users' information. The experiments with MovieLen data indicate substantial and consistent improvements of this model in overcoming the cold-start user-side problem.},
  isbn = {978-1-59593-993-7},
  keywords = {aspect model,cold-start,collaborative filtering,information filtering,three-way aspect model,triadic aspect model}
}

@misc{masnickProtocolsNotPlatforms,
  title = {Protocols, {{Not Platforms}}: {{A Technological Approach}} to {{Free Speech}}},
  shorttitle = {Protocols, {{Not Platforms}}},
  author = {Masnick, Mike},
  urldate = {2022-04-21},
  howpublished = {https://knightcolumbia.org/content/protocols-not-platforms-a-technological-approach-to-free-speech},
  langid = {english}
}

@misc{mastodonggmbhServers,
  title = {Servers},
  author = {{Mastodon gGmbH}},
  journal = {Join Mastodon},
  urldate = {2024-03-04},
  abstract = {Find where to sign up for the decentralized social network Mastodon.},
  howpublished = {https://joinmastodon.org/servers},
  langid = {english}
}

@article{newellUserMigrationOnline2021,
  title = {User {{Migration}} in {{Online Social Networks}}: {{A Case Study}} on {{Reddit During}} a {{Period}} of {{Community Unrest}}},
  author = {Newell, Edward and Jurgens, David and Saleem, Haji Mohammad and Vala, Hardik and Sassine, Jad and Armstrong, Caitrin and Ruths, Derek},
  year = {2021},
  month = aug,
  journal = {Proceedings of the International AAAI Conference on Web and Social Media},
  pages = {279--288},
  doi = {10.1609/icwsm.v10i1.14750},
  abstract = {Platforms like Reddit have attracted large and vibrant communities, but the individuals in those communities are free to migrate to other platforms at any time. History has borne this out with the mass migration from Slashdot to Digg. The underlying motivations of individuals who migrate between platforms, and the conditions that favor migration online are not well-understood. We examine Reddit during a period of community unrest affecting millions of users in the summer of 2015, and analyze large-scale changes in user behavior and migration patterns to Reddit-like alternative platforms. Using self-reported statements from user comments, surveys, and a computational analysis of the activity of users with accounts on multiple platforms, we identify the primary motivations driving user migration. While a notable number of Reddit users left for other platforms, we found that an important pull factor that enabled Reddit to retain users was its long tail of niche content. Other platforms may reach critical mass to support popular or ``mainstream'' topics, but Reddit's large userbase provides a key advantage in supporting niche topics.},
  langid = {english}
}

@inproceedings{nicholsonMastodonRulesCharacterizing2023,
  title = {Mastodon {{Rules}}: {{Characterizing Formal Rules}} on {{Popular Mastodon Instances}}},
  shorttitle = {Mastodon {{Rules}}},
  booktitle = {Companion {{Publication}} of the 2023 {{Conference}} on {{Computer Supported Cooperative Work}} and {{Social Computing}}},
  author = {Nicholson, Matthew N. and Keegan, Brian C and Fiesler, Casey},
  year = {2023},
  month = oct,
  series = {{{CSCW}} '23 {{Companion}}},
  pages = {86--90},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  doi = {10.1145/3584931.3606970},
  urldate = {2024-01-16},
  abstract = {Federated social networking is an increasingly popular alternative to more traditional, centralized forms. Yet, this federated arrangement can lead to dramatically different experiences across the network. Using a sample of the most popular instances on the federated social network Mastodon, we characterize the types of rules present in this emerging space. We then compare these rules to those on Reddit, as an example of a different, less centralized space. Rules on Mastodon often pay particular attention to issues of harassment and hate --- strongly reflecting the spirit of the Mastodon Covenant. We speculate that these rules may have emerged in response to problems of other platforms, and reflect a lack of support for instance maintainers. With this work, we call for the development of additional instance-level governance and technical scaffolding, and raise questions for future work into the development, values, and value tensions present in the broader federated social networking landscape.},
  isbn = {9798400701290},
  keywords = {community rules,Mastodon,online communities}
}

@article{paterekImprovingRegularizedSingular2007,
  title = {Improving Regularized Singular Value Decomposition for Collaborative Filtering},
  author = {Paterek, Arkadiusz},
  year = {2007},
  month = aug,
  journal = {Proceedings of KDD cup and workshop},
  abstract = {A key part of a recommender system is a collaborative filtering algorithm predicting users' preferences for items. In this paper we describe different efficient collaborative filtering techniques and a framework for combining them to obtain a good prediction.},
  langid = {english}
}

@inproceedings{ramanChallengesDecentralisedWeb2019a,
  title = {Challenges in the {{Decentralised Web}}: {{The Mastodon Case}}},
  shorttitle = {Challenges in the {{Decentralised Web}}},
  booktitle = {Proceedings of the {{Internet Measurement Conference}}},
  author = {Raman, Aravindh and Joglekar, Sagar and Cristofaro, Emiliano De and Sastry, Nishanth and Tyson, Gareth},
  year = {2019},
  month = oct,
  series = {{{IMC}} '19},
  pages = {217--229},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  doi = {10.1145/3355369.3355572},
  urldate = {2024-03-06},
  abstract = {The Decentralised Web (DW) has recently seen a renewed momentum, with a number of DW platforms like Mastodon, PeerTube, 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 re-centralisation. 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.},
  isbn = {978-1-4503-6948-0}
}

@inproceedings{resnickGrouplensOpenArchitecture1994,
  title = {Grouplens: An Open Architecture for Collaborative Filtering of Netnews},
  shorttitle = {Grouplens},
  booktitle = {Proceedings of the 1994 {{ACM Conference}} on {{Computer Supported Cooperative Work}}},
  author = {Resnick, Paul and Iacovou, Neophytos and Suchak, Mitesh and Bergstrom, Peter and Riedl, John},
  year = {1994},
  series = {{{CSCW}} '94},
  pages = {175--186},
  publisher = {ACM},
  address = {New York, NY, USA},
  doi = {10.1145/192844.192905},
  urldate = {2016-07-19},
  abstract = {Collaborative filters help people make choices based on the opinions of other people. GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles. News reader clients display predicted scores and make it easy for users to rate articles after they read them. Rating servers, called Better Bit Bureaus, gather and disseminate the ratings. The rating servers predict scores based on the heuristic that people who agreed in the past will probably agree again. Users can protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction. The entire architecture is open: alternative software for news clients and Better Bit Bureaus can be developed  independently and can interoperate with the components we have developed.},
  isbn = {978-0-89791-689-9}
}

@book{ricciRecommenderSystemsHandbook2022,
  title = {Recommender Systems Handbook},
  editor = {Ricci, Francesco and Ro{\d k}a{\d h}, Liʾor and Shapira, Bracha},
  year = {2022},
  edition = {Third edition},
  publisher = {Springer},
  address = {New York, NY},
  abstract = {This third edition handbook describes in detail the classical methods as well as extensions and novel approaches that were more recently introduced within this field. It consists of five parts: general recommendation techniques, special recommendation techniques, value and impact of recommender systems, human computer interaction, and applications. The first part presents the most popular and fundamental techniques currently used for building recommender systems, such as collaborative filtering, semantic-based methods, recommender systems based on implicit feedback, neural networks and context-aware methods. The second part of this handbook introduces more advanced recommendation techniques, such as session-based recommender systems, adversarial machine learning for recommender systems, group recommendation techniques, reciprocal recommenders systems, natural language techniques for recommender systems and cross-domain approaches to recommender systems. The third part covers a wide perspective to the evaluation of recommender systems with papers on methods for evaluating recommender systems, their value and impact, the multi-stakeholder perspective of recommender systems, the analysis of the fairness, novelty and diversity in recommender systems. The fourth part contains a few chapters on the human computer dimension of recommender systems, with research on the role of explanation, the user personality and how to effectively support individual and group decision with recommender systems. The last part focusses on application in several important areas, such as, food, music, fashion and multimedia recommendation. This informative third edition handbook provides a comprehensive, yet concise and convenient reference source to recommender systems for researchers and advanced-level students focused on computer science and data science. Professionals working in data analytics that are using recommendation and personalization techniques will also find this handbook a useful tool},
  isbn = {978-1-07-162196-7 978-1-07-162199-8},
  langid = {english}
}

@article{robertsonProbabilisticRelevanceFramework2009,
  title = {The {{Probabilistic Relevance Framework}}: {{BM25}} and {{Beyond}}},
  shorttitle = {The {{Probabilistic Relevance Framework}}},
  author = {Robertson, Stephen and Zaragoza, Hugo},
  year = {2009},
  journal = {Foundations and Trends{\textregistered} in Information Retrieval},
  volume = {3},
  number = {4},
  pages = {333--389},
  issn = {1554-0669, 1554-0677},
  doi = {10.1561/1500000019},
  urldate = {2024-05-20},
  abstract = {The Probabilistic Relevance Framework (PRF) is a formal framework for document retrieval, grounded in work done in the 1970--1980s, which led to the development of one of the most successful text-retrieval algorithms, BM25. In recent years, research in the PRF has yielded new retrieval models capable of taking into account document meta-data (especially structure and link-graph information). Again, this has led to one of the most successful Web-search and corporate-search algorithms, BM25F. This work presents the PRF from a conceptual point of view, describing the probabilistic modelling assumptions behind the framework and the different ranking algorithms that result from its application: the binary independence model, relevance feedback models, BM25 and BM25F. It also discusses the relation between the PRF and other statistical models for IR, and covers some related topics, such as the use of non-textual features, and parameter optimisation for models with free parameters.},
  langid = {english}
}

@misc{rochkoMastodon2023,
  title = {Mastodon 4.2},
  author = {Rochko, Eugen},
  year = {2023},
  month = sep,
  journal = {Mastodon Blog},
  urldate = {2024-03-06},
  abstract = {In this massive update we've added search and removed friction. What's not to love?},
  howpublished = {https://blog.joinmastodon.org/2023/09/mastodon-4.2/}
}

@misc{rochkoNewOnboardingExperience2023,
  title = {A New Onboarding Experience on {{Mastodon}}},
  author = {Rochko, Eugen},
  year = {2023},
  month = may,
  journal = {Mastodon Blog},
  urldate = {2024-03-04},
  abstract = {Today we're making signing up on Mastodon easier than ever before. We understand that deciding which Mastodon service provider to kick off your experience with can be confusing. We know this is a completely new concept for many people, since traditionally the platform and the service provider are one and the same. This choice is what makes Mastodon different from existing social networks, but it also presents a unique onboarding challenge.},
  howpublished = {https://blog.joinmastodon.org/2023/05/a-new-onboarding-experience-on-mastodon/}
}

@misc{rothItGettingEasier2023,
  title = {It's Getting Easier to Make an Account on {{Mastodon}}},
  author = {Roth, Emma},
  year = {2023},
  month = may,
  journal = {The Verge},
  urldate = {2024-03-04},
  abstract = {The network lets you sign up for mastodon.social from the start.},
  howpublished = {https://www.theverge.com/2023/5/1/23707019/mastodon-account-creation-twitter-alternative},
  langid = {english}
}

@book{saltonIntroductionModernInformation1987,
  title = {Introduction to Modern Information Retrieval},
  author = {Salton, Gerard and McGill, Michael J.},
  year = {1987},
  series = {{{McGraw-Hill}} International Editions},
  edition = {3. pr},
  publisher = {McGraw-Hill Book Comp},
  address = {New York},
  isbn = {978-0-07-054484-0},
  langid = {english}
}

@inproceedings{sarwarItembasedCollaborativeFiltering2001,
  title = {Item-Based Collaborative Filtering Recommendation Algorithms},
  booktitle = {Proceedings of the 10th International Conference on {{World Wide Web}}},
  author = {Sarwar, Badrul and Karypis, George and Konstan, Joseph and Riedl, John},
  year = {2001},
  month = apr,
  series = {{{WWW}} '01},
  pages = {285--295},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  doi = {10.1145/371920.372071},
  urldate = {2024-05-07},
  isbn = {978-1-58113-348-6}
}

@incollection{schaferCollaborativeFilteringRecommender2007,
  title = {Collaborative Filtering Recommender Systems},
  booktitle = {The {{Adaptive Web}}: {{Methods}} and {{Strategies}} of {{Web Personalization}}},
  author = {Schafer, J. Ben and Frankowski, Dan and Herlocker, Jon and Sen, Shilad},
  editor = {Brusilovsky, Peter and Kobsa, Alfred and Nejdl, Wolfgang},
  year = {2007},
  series = {Lecture {{Notes}} in {{Computer Science}}},
  pages = {291--324},
  publisher = {Springer},
  address = {Berlin, Heidelberg},
  doi = {10.1007/978-3-540-72079-9_9},
  urldate = {2020-08-06},
  abstract = {One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substantial quantities of data. In this chapter we introduce the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings. We also discuss how to evaluate CF systems, and the evolution of rich interaction interfaces. We close the chapter with discussions of the challenges of privacy particular to a CF recommendation service and important open research questions in the field.},
  isbn = {978-3-540-72079-9},
  langid = {english},
  keywords = {Association Rule Mining,Collaborative Filter,Explicit Rating,News Article,Recommender System}
}

@misc{silberlingBeginnerGuideMastodon2023,
  title = {A Beginner's Guide to {{Mastodon}}, the Open Source {{Twitter}} Alternative {\textbar} {{TechCrunch}}},
  author = {Silberling, Amanda},
  year = {2023},
  month = jul,
  journal = {TechCrunch},
  urldate = {2024-03-04},
  howpublished = {https://techcrunch.com/2023/07/24/what-is-mastodon/}
}

@article{suSurveyCollaborativeFiltering2009,
  title = {A {{Survey}} of {{Collaborative Filtering Techniques}}},
  author = {Su, Xiaoyuan and Khoshgoftaar, Taghi M.},
  year = {2009},
  month = oct,
  journal = {Advances in Artificial Intelligence},
  volume = {2009},
  pages = {e421425},
  publisher = {Hindawi},
  issn = {1687-7470},
  doi = {10.1155/2009/421425},
  urldate = {2024-05-09},
  abstract = {As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, model-based, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.},
  langid = {english}
}

@inproceedings{teblunthuisIdentifyingCompetitionMutualism2022,
  title = {Identifying Competition and Mutualism between Online Groups},
  booktitle = {International {{AAAI Conference}} on {{Web}} and {{Social Media}} ({{ICWSM}} 2022)},
  author = {TeBlunthuis, Nathan and Hill, Benjamin Mako},
  year = {2022},
  month = jun,
  volume = {16},
  pages = {993--1004},
  publisher = {AAAI},
  address = {Atlanta, Georgia, USA},
  urldate = {2021-07-16},
  abstract = {Platforms often host multiple online groups with highly overlapping topics and members. How can researchers and designers understand how interactions between related groups affect measures of group health? Inspired by population ecology, prior social computing research has studied competition and mutualism among related groups by correlating group size with degrees of overlap in content and membership. The resulting body of evidence is puzzling as overlaps seem sometimes to help and other times to hurt. We suggest that this confusion results from aggregating inter-group relationships into an overall environmental effect instead of focusing on networks of competition and mutualism among groups. We propose a theoretical framework based on community ecology and a method for inferring competitive and mutualistic interactions from time series participation data. We compare population and community ecology analyses of online community growth by analyzing clusters of subreddits with high user overlap but varying degrees of competition and mutualism.},
  keywords = {Computer Science - Human-Computer Interaction,Computer Science - Social and Information Networks}
}

@misc{thekinrarMastodonInstances2017,
  title = {Mastodon Instances},
  author = {TheKinrar},
  year = {2017},
  journal = {instances.social},
  urldate = {2024-03-04},
  howpublished = {https://instances.social/}
}

@misc{trienesRecommendingUsersWhom2018,
  title = {Recommending {{Users}}: {{Whom}} to {{Follow}} on {{Federated Social Networks}}},
  shorttitle = {Recommending {{Users}}},
  author = {Trienes, Jan and Cano, Andr{\'e}s Torres and Hiemstra, Djoerd},
  year = {2018},
  month = nov,
  number = {arXiv:1811.09292},
  eprint = {1811.09292},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.1811.09292},
  urldate = {2024-03-06},
  abstract = {To foster an active and engaged community, social networks employ recommendation algorithms that filter large amounts of contents and provide a user with personalized views of the network. Popular social networks such as Facebook and Twitter generate follow recommendations by listing profiles a user may be interested to connect with. Federated social networks aim to resolve issues associated with the popular social networks - such as large-scale user-surveillance and the miss-use of user data to manipulate elections - by decentralizing authority and promoting privacy. Due to their recent emergence, recommender systems do not exist for federated social networks, yet. To make these networks more attractive and promote community building, we investigate how recommendation algorithms can be applied to decentralized social networks. We present an offline and online evaluation of two recommendation strategies: a collaborative filtering recommender based on BM25 and a topology-based recommender using personalized PageRank. Our experiments on a large unbiased sample of the federated social network Mastodon shows that collaborative filtering approaches outperform a topology-based approach, whereas both approaches significantly outperform a random recommender. A subsequent live user experiment on Mastodon using balanced interleaving shows that the collaborative filtering recommender performs on par with the topology-based recommender.},
  archiveprefix = {arxiv},
  keywords = {Computer Science - Information Retrieval,Computer Science - Social and Information Networks}
}

@article{webberSimilarityMeasureIndefinite2010,
  title = {A Similarity Measure for Indefinite Rankings},
  author = {Webber, William and Moffat, Alistair and Zobel, Justin},
  year = {2010},
  month = nov,
  journal = {ACM Transactions on Information Systems},
  volume = {28},
  number = {4},
  pages = {20:1--20:38},
  issn = {1046-8188},
  doi = {10.1145/1852102.1852106},
  urldate = {2024-02-14},
  abstract = {Ranked lists are encountered in research and daily life and it is often of interest to compare these lists even when they are incomplete or have only some members in common. An example is document rankings returned for the same query by different search engines. A measure of the similarity between incomplete rankings should handle nonconjointness, weight high ranks more heavily than low, and be monotonic with increasing depth of evaluation; but no measure satisfying all these criteria currently exists. In this article, we propose a new measure having these qualities, namely rank-biased overlap (RBO). The RBO measure is based on a simple probabilistic user model. It provides monotonicity by calculating, at a given depth of evaluation, a base score that is non-decreasing with additional evaluation, and a maximum score that is nonincreasing. An extrapolated score can be calculated between these bounds if a point estimate is required. RBO has a parameter which determines the strength of the weighting to top ranks. We extend RBO to handle tied ranks and rankings of different lengths. Finally, we give examples of the use of the measure in comparing the results produced by public search engines and in assessing retrieval systems in the laboratory.},
  keywords = {probabilistic models,Rank correlation,ranking}
}

@article{zangerleEvaluatingRecommenderSystems2022,
  title = {Evaluating {{Recommender Systems}}: {{Survey}} and {{Framework}}},
  shorttitle = {Evaluating {{Recommender Systems}}},
  author = {Zangerle, Eva and Bauer, Christine},
  year = {2022},
  month = dec,
  journal = {ACM Computing Surveys},
  volume = {55},
  number = {8},
  pages = {170:1--170:38},
  issn = {0360-0300},
  doi = {10.1145/3556536},
  urldate = {2024-05-07},
  abstract = {The comprehensive evaluation of the performance of a recommender system is a complex endeavor: many facets need to be considered in configuring an adequate and effective evaluation setting. Such facets include, for instance, defining the specific goals of the evaluation, choosing an evaluation method, underlying data, and suitable evaluation metrics. In this article, we consolidate and systematically organize this dispersed knowledge on recommender systems evaluation. We introduce the Framework for Evaluating Recommender systems (FEVR), which we derive from the discourse on recommender systems evaluation. In FEVR, we categorize the evaluation space of recommender systems evaluation. We postulate that the comprehensive evaluation of a recommender system frequently requires considering multiple facets and perspectives in the evaluation. The FEVR framework provides a structured foundation to adopt adequate evaluation configurations that encompass this required multi-facetedness and provides the basis to advance in the field. We outline and discuss the challenges of a comprehensive evaluation of recommender systems and provide an outlook on what we need to embrace and do to move forward as a research community.},
  keywords = {FEVR,Framework for EValuating Recommender systems,Survey}
}

@inproceedings{zhuPopularityOpportunityBiasCollaborative2021,
  title = {Popularity-{{Opportunity Bias}} in {{Collaborative Filtering}}},
  booktitle = {Proceedings of the 14th {{ACM International Conference}} on {{Web Search}} and {{Data Mining}}},
  author = {Zhu, Ziwei and He, Yun and Zhao, Xing and Zhang, Yin and Wang, Jianling and Caverlee, James},
  year = {2021},
  month = mar,
  series = {{{WSDM}} '21},
  pages = {85--93},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  doi = {10.1145/3437963.3441820},
  urldate = {2024-05-21},
  abstract = {This paper connects equal opportunity to popularity bias in implicit recommenders to introduce the problem of popularity-opportunity bias. That is, conditioned on user preferences that a user likes both items, the more popular item is more likely to be recommended (or ranked higher) to the user than the less popular one. This type of bias is harmful, exerting negative effects on the engagement of both users and item providers. Thus, we conduct a three-part study: (i) By a comprehensive empirical study, we identify the existence of the popularity-opportunity bias in fundamental matrix factorization models on four datasets; (ii) coupled with this empirical study, our theoretical study shows that matrix factorization models inherently produce the bias; and (iii) we demonstrate the potential of alleviating this bias by both in-processing and post-processing algorithms. Extensive experiments on four datasets show the effective debiasing performance of these proposed methods compared with baselines designed for conventional popularity bias.},
  isbn = {978-1-4503-8297-7},
  keywords = {equal opportunity,recommendation bias,recommender systems,statistical parity}
}

@article{zulliRethinkingSocialSocial2020,
  title = {Rethinking the ``Social'' in ``Social Media'': {{Insights}} into Topology, Abstraction, and Scale on the {{Mastodon}} Social Network},
  shorttitle = {Rethinking the ``Social'' in ``Social Media''},
  author = {Zulli, Diana and Liu, Miao and Gehl, Robert},
  year = {2020},
  month = jul,
  journal = {New Media \& Society},
  volume = {22},
  number = {7},
  pages = {1188--1205},
  publisher = {SAGE Publications},
  issn = {1461-4448},
  doi = {10.1177/1461444820912533},
  urldate = {2022-03-13},
  abstract = {Online interactions are often understood through the corporate social media (CSM) model where social interactions are determined through layers of abstraction and centralization that eliminate users from decision-making processes. This study demonstrates how alternative social media (ASM)?namely Mastodon?restructure the relationship between the technical structure of social media and the social interactions that follow, offering a particular type of sociality distinct from CSM. Drawing from a variety of qualitative data, this analysis finds that (1) the decentralized structure of Mastodon enables community autonomy, (2) Mastodon?s open-source protocol allows the internal and technical development of the site to become a social enterprise in and of itself, and (3) Mastodon?s horizontal structure shifts the site?s scaling focus from sheer number of users to quality engagement and niche communities. To this end, Mastodon helps us rethink ?the social? in social media in terms of topology, abstraction, and scale.}
}
