![]() ![]() The source code for this case study is shared through a public repository for readers to replicate this analysis and use it as a template for their own research. We then present a case study to demonstrate Botometer usage. We start with an introduction to how Botometer works and how users can access it. ![]() This tutorial is designed for data scientists and computational social scientists who might not be familiar with Botometer, the machine learning methods behind it, its programmatic interface, or how to interpret its results. Many researchers have applied Botometer in their studies to directly investigate social bots and their impact, or to distinguish human accounts and bot-like accounts in order to better address their questions of interest. Finally, Botometer has been extensively validated in the field. It handles around a quarter million daily queries-over half a billion in total since its inception. The API has a nominal fee for heavy use, which discourages abuse and partially offsets infrastructure and maintenance costs. Anyone with a Twitter account can use the web version for free researchers with Twitter developer accounts can use the API endpoints to analyze large-scale datasets. Second, Botometer is easily accessible through both a web interface and an application programming interface (API). It has also been routinely upgraded to stay accurate and relevant. First, it is well maintained and has been serving the community for the past seven years without major outages. Although other bot detection tools such as tweetbotornot Footnote 2 and Bot Sentinel Footnote 3 exist, we focus on Botometer for several reasons. This practicum aims to provide a tutorial for Botometer, a machine learning tool for bot detection on Twitter. It is therefore crucial for researchers to have access to a reliable tool for detecting social bots. ![]() Second, researchers need to properly handle bots in their data since their presence may distort analyses. First, characterizing the behavior of and assessing the impact of social bots remains an interesting research topic. This has two implications for researchers. Although Twitter strengthened their efforts to contain malicious actors in recent years, Footnote 1 deceptive bots remain prevalent and display evolving tactics to evade detection. Most existing reports and studies on social bots focus on Twitter, largely because its data can be easily accessed. In some cases, bots form dense social networks to boost engagement and popularity metrics and to amplify each other’s messages. In terms of content, malicious bots are found to engage other accounts with negative and inflammatory language or hate speech. Some bots act as fake followers to inflate the popularity of other accounts. They can also disseminate low-credibility information strategically by getting involved in the early stage of the spreading process and targeting popular users through mentions and replies. They may simply generate a large volume of posts to amplify certain narratives or to manipulate the price of stocks and cryptocurrencies. Malicious social bots demonstrate various behavioral patterns in their actions. Research has also reported on the presence of social bots in discussions about climate change, cryptocurrency, and the stock market. Bots also actively participate in public health debates including those about vaccines, the COVID-19 pandemic, and cannabis. elections, French elections, the Brexit referendum, German elections, and the 2017 Catalan referendum. Studies have identified interference of social bots in U.S. Such malicious bots are involved with all types of online discussions, especially controversial ones. While many social bots are benign, malicious bots can deceptively impersonate humans to manipulate and pollute the information ecosystem. Social bots are social media accounts controlled in part by software that can post content and interact with other accounts programmatically and possibly automatically. ![]()
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