@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{cavaDriversSocialInfluence2023,
  title = {Drivers of Social Influence in the {{Twitter}} Migration to {{Mastodon}}},
  author = {Cava, Lucio La 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}
}

@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}
}

@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{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.}
}

@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{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}
}

@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{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 {\textemdash} 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}
}

@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}
}

@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{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.}
}
