ArticleJournal of computational social science2022
Botometer 101: social bot practicum for computational social scientists.
Article in Journal of computational social science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 146 citations in OpenAlex.
- Emotionally based strategic communications as a new tool in defensive cognitive warfare.Frontiers in psychology · 2026Review
- Emoji-Driven Sentiment Analysis for Social Bot Detection with Relational Graph Convolutional Networks.Sensors (Basel, Switzerland) · 2025Article
- CB-MTE: Social Bot Detection via Multi-Source Heterogeneous Feature Fusion.Sensors (Basel, Switzerland) · 2025Article
- Quantifying Bot Impact: An Information-Theoretic Analysis of Complexity and Uncertainty in Online Political Communication Dynamics.Entropy (Basel, Switzerland) · 2025Article
- Not our kind of crowd! How partisan bias distorts perceptions of political bots on Twitter (now X).The British journal of social psychology · 2025Article
- A global comparison of social media bot and human characteristics.Scientific reports · 2025Article
- Differential impact from individual versus collective misinformation tagging on the diversity of Twitter (X) information engagement and mobility.Nature communications · 2025Article
- A perspective on friction interventions to curb the spread of misinformation.Npj complexity · 2025Review
- Improving Social Bot Detection Through Aid and Training.Human factors · 2024Article
- From Tweets to Streets: Observational Study on the Association Between Twitter Sentiment and Anti-Asian Hate Crimes in New York City from 2019 to 2022.Journal of medical Internet research · 2024Observational
- SEGCN: a subgraph encoding based graph convolutional network model for social bot detection.Scientific reports · 2024Article
- Deciphering Influence on Social Media: A Comparative Analysis of Influential Account Detection Metrics in the Context of Tobacco Promotion.Social media + society · 2024Article
- A graph neural architecture search approach for identifying bots in social media.Frontiers in artificial intelligence · 2024Article
- Comparing methods for creating a national random sample of twitter users.Social network analysis and mining · 2024Article
- Detecting anomalies in graph networks on digital markets.PloS one · 2024Article
- Evaluative altmetrics: is there evidence for its application to research evaluation?Frontiers in research metrics and analytics · 2023Article
- Social media engagement and cryptocurrency performance.PloS one · 2023Article
- Analysis and mining of an election-based network using large-scale twitter data: a retrospective study.Social network analysis and mining · 2023Article
- How does Twitter account moderation work? Dynamics of account creation and suspension on Twitter during major geopolitical events.EPJ data science · 2023Article
- Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic.International journal of environmental research and public health · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Social bots have become an important component of online social media. Deceptive bots, in particular, can manipulate online discussions of important issues ranging from elections to public health, threatening the constructive exchange of information. Their ubiquity makes them an interesting research subject and requires researchers to properly handle them when conducting studies using social media data. Therefore, it is important for researchers to gain access to bot detection tools that are reliable and easy to use. This paper aims to provide an introductory tutorial of Botometer, a public tool for bot detection on Twitter, for readers who are new to this topic and may not be familiar with programming and machine learning. We introduce how Botometer works, the different ways users can access it, and present a case study as a demonstration. Readers can use the case study code as a template for their own research. We also discuss recommended practice for using Botometer.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.