  Twitter has emerged as a global hub for engaging in online conversations and
as a research corpus for various disciplines that have recognized the
significance of its user-generated content. Argument mining is an important
analytical task for processing and understanding online discourse.
Specifically, it aims to identify the structural elements of arguments, denoted
as information and inference. These elements, however, are not static and may
require context within the conversation they are in, yet there is a lack of
data and annotation frameworks addressing this dynamic aspect on Twitter. We
contribute TACO, the first dataset of Twitter Arguments utilizing 1,814 tweets
covering 200 entire conversations spanning six heterogeneous topics annotated
with an agreement of 0.718 Krippendorff's alpha among six experts. Second, we
provide our annotation framework, incorporating definitions from the Cambridge
Dictionary, to define and identify argument components on Twitter. Our
transformer-based classifier achieves an 85.06\% macro F1 baseline score in
detecting arguments. Moreover, our data reveals that Twitter users tend to
engage in discussions involving informed inferences and information. TACO
serves multiple purposes, such as training tweet classifiers to manage tweets
based on inference and information elements, while also providing valuable
insights into the conversational reply patterns of tweets.
