论文标题
BirdsPotter：用于分析和标记Twitter用户的工具
Birdspotter: A Tool for Analyzing and Labeling Twitter Users
论文作者
论文摘要
在线社交媒体对社会活动和机构的影响是深远的。随着用户吸收的迅速增加，我们才刚刚开始了解其后果。将在线话语模拟为现实世界行为的代理的社会科学家和从业人员通常会策划大型社交媒体数据集。缺乏针对非DATA科学专家的可用工具经常使这些数据（以及它所拥有的见解）不足。在这里，我们提出了鸟斑 - 分析和标记Twitter用户的工具，以及birdspotter.ml-计算机指标的探索性可视化器。 BirdsPotter提供了一条端到端分析管道，从预采用的Twitter数据的处理，到用户的通用标签，并在几行代码中估算其社会影响力。该软件包具有教程和详细文档。我们还说明了如何将BirdsPotter训练成一个成熟的机器人检测器，该探测器比不进行任何Twitter API在线呼叫而获得的优于最先进的表演，我们在对主题COVID-19数据集的探索性分析中展示了其用法。
The impact of online social media on societal events and institutions is profound; and with the rapid increases in user uptake, we are just starting to understand its ramifications. Social scientists and practitioners who model online discourse as a proxy for real-world behavior, often curate large social media datasets. A lack of available tooling aimed at non-data science experts frequently leaves this data (and the insights it holds) underutilized. Here, we propose birdspotter -- a tool to analyze and label Twitter users --, and birdspotter.ml -- an exploratory visualizer for the computed metrics. birdspotter provides an end-to-end analysis pipeline, from the processing of pre-collected Twitter data, to general-purpose labeling of users, and estimating their social influence, within a few lines of code. The package features tutorials and detailed documentation. We also illustrate how to train birdspotter into a fully-fledged bot detector that achieves better than state-of-the-art performances without making any Twitter API online calls, and we showcase its usage in an exploratory analysis of a topical COVID-19 dataset.
