Collecting and Analyzing Public Data from Mastodon
Pages 5551 - 5553
Abstract
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.
References
[1]
Ali Abdallah Alalwan, Nripendra P Rana, Yogesh K Dwivedi, and Raed Algharabat. 2017. Social media in marketing: A review and analysis of the existing literature. Telematics and informatics, Vol. 34, 7 (2017), 1177--1190.
[2]
Bogdan Batrinca and Philip C Treleaven. 2015. Social media analytics: a survey of techniques, tools and platforms. Ai & Society, Vol. 30 (2015), 89--116.
[3]
Jiahui He, Haris Bin Zia, Ignacio Castro, Aravindh Raman, Nishanth Sastry, and Gareth Tyson. 2023. Flocking to mastodon: Tracking the great twitter migration. In Proceedings of the 2023 ACM on Internet Measurement Conference. 111--123.
[4]
Lucio La Cava, Sergio Greco, and Andrea Tagarelli. 2021. Understanding the growth of the Fediverse through the lens of Mastodon. Applied network science, Vol. 6 (2021), 1--35.
[5]
Tyler H McCormick, Hedwig Lee, Nina Cesare, Ali Shojaie, and Emma S Spiro. 2017. Using Twitter for demographic and social science research: Tools for data collection and processing. Sociological methods & research, Vol. 46, 3 (2017), 390--421.
[6]
Nicholas Proferes, Naiyan Jones, Sarah Gilbert, Casey Fiesler, and Michael Zimmer. 2021. Studying reddit: A systematic overview of disciplines, approaches, methods, and ethics. Social Media Society, Vol. 7, 2 (2021), 20563051211019004.
[7]
Diana Rieger, Anna Sophie Kümpel, Maximilian Wich, Toni Kiening, and Georg Groh. 2021. Assessing the extent and types of hate speech in fringe communities: A case study of alt-right communities on 8chan, 4chan, and Reddit. Social Media Society, Vol. 7, 4 (2021), 20563051211052906.
[8]
Zeerak Waseem and Dirk Hovy. 2016. Hateful symbols or hateful people? predictive features for hate speech detection on twitter. In Proceedings of the NAACL student research workshop. 88--93.
[9]
XDevelopers. 2023. https://x.com/XDevelopers/status/1621026986784337922.
Index Terms
- Collecting and Analyzing Public Data from Mastodon
Recommendations
Flocking to Mastodon: Tracking the Great Twitter Migration
IMC '23: Proceedings of the 2023 ACM on Internet Measurement ConferenceThe 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 ...
Mastodoner: A Command-line Tool and Python Library for Public Data Collection from Mastodon
CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge ManagementThis 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 ...
Techniques for Collecting data in Social Networks
NBIS '14: Proceedings of the 2014 17th International Conference on Network-Based Information SystemsData is a piece of information that is needed to form useful information. Obtaining data is needed by individuals such as researchers. From this perspective, data collection is an important step when doing any research or experiment. Data collection can ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
October 2024
5705 pages
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 21 October 2024
Check for updates
Author Tags
Qualifiers
- Tutorial
Conference
CIKM '24
Sponsor:
CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management
October 21 - 25, 2024
ID, Boise, USA
Acceptance Rates
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%
Upcoming Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 63Total Downloads
- Downloads (Last 12 months)63
- Downloads (Last 6 weeks)45
Reflects downloads up to 12 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in