  Online news and social media have been the de facto mediums to disseminate
information globally from the beginning of the last decade. However, bias in
content and purpose of intentions are not regulated, and managing bias is the
responsibility of content consumers. In this regard, understanding the stances
and biases of news sources towards specific entities becomes important. To
address this problem, we use pretrained language models, which have been shown
to bring about good results with no task-specific training or few-shot
training. In this work, we approach the problem of characterizing Named
Entities and Tweets as an open-ended text classification and open-ended fact
probing problem.We evaluate the zero-shot language model capabilities of
Generative Pretrained Transformer 2 (GPT-2) to characterize Entities and Tweets
subjectively with human psychology-inspired and logical conditional prefixes
and contexts. First, we fine-tune the GPT-2 model on a sufficiently large news
corpus and evaluate subjective characterization of popular entities in the
corpus by priming with prefixes. Second, we fine-tune GPT-2 with a Tweets
corpus from a few popular hashtags and evaluate characterizing tweets by
priming the language model with prefixes, questions, and contextual synopsis
prompts. Entity characterization results were positive across measures and
human evaluation.
