Autotootmine

Autotootmine is the automated collection and analysis of public toots from federated social networks such as Mastodon. It involves harvesting public timelines or real-time streams, then processing and analyzing the text to extract trends, linguistic features, sentiment, or other signals. The term is used in academic, journalistic, and product research contexts to describe end-to-end pipelines that scale beyond manual data gathering.

A typical autotootmine workflow uses an application that authenticates with the platform’s API, subscribes to relevant timelines or streams, and applies filters for language, keywords, or specific accounts. Collected toots and metadata are stored in a database, while text is preprocessed and analyzed with natural language processing and machine learning techniques to identify topics, sentiment, emotion, influence, and network relationships. Outputs often include dashboards, research datasets, or automated alerts for moderation or trend detection.

Applications of autotootmine include sociolinguistic studies, public opinion monitoring, early detection of events or crises, market and brand research, and support for moderation and content policy work. It can help researchers map discourse, measure change over time, or compare activity across instances within the Fediverse.

Limitations and ethical considerations are significant. Public availability of data does not imply universal consent, and researchers must respect platform terms of service, data retention policies, and user privacy. Data quality can vary across instances, and federation differences can bias results. There is also a risk of misinterpretation, overgeneralization, or unintended consequences for users when datasets are shared or reused.

See also: Mastodon, Fediverse, social media analytics, data ethics, natural language processing.