Profiling Cryptocurrency Influencers with Few-shot Learning 2023

Sponsored by
Symanto Research

Synopsis

  • Task: In this shared task we aim to profile cryptocurrency influencers in social media, from a low-resource perspective. Moreover, we propose to categorize other related aspects of the influencers, also using a low-resource setting. Specifically, we focus on English Twitter posts for three different sub-tasks:
    • Low-resource influencer profiling
    • Low-resource influencer interest identification
    • Low-resource influencer intent identification
    Participation for independent tasks, both from machine and deep learning paradigms, are welcome.
  • Input: tba.
  • Evaluation: tba.
  • Submission: TIRA.
  • Baselines: tba.

Task

Data annotation for Natural Language Processing (NLP) is a challenging task. Aspects such as the economic and temporal cost, the psychological and linguistic expertise needed by the annotator, and the congenital subjectivity involved in the annotation task, makes it difficult to obtain large amounts of high quality data [1, 2].
Cryptocurrencies have massively increased their popularity in recent years [3]. Aspects such as not being reliant on any central authority, the possibilities offered by the different projects, and the new gold rush, spread mainly by influencers, make this a very trendy topic in social media. However, in a real environment where, for instance, traders may want to leverage social media signals to forecast the market, data collection is a challenge and real-time profiling needs to be done in a few milliseconds, which implies to process as little data as possible.
Participants will be provided with little training data per task, and will need to choose carefully the models applied to this under-resource setting. Concepts such as transfer learning [4] and few-shot learning [5,6,7,8] will be key to excel.

Task Committee