  Extreme pricing anomalies may occur unexpectedly without a trivial cause, and
equity traders typically experience a meticulous process to source disparate
information and analyze its reliability before integrating it into the trusted
knowledge base. We introduce DeepTrust, a reliable financial knowledge
retrieval framework on Twitter to explain extreme price moves at speed, while
ensuring data veracity using state-of-the-art NLP techniques. Our proposed
framework consists of three modules, specialized for anomaly detection,
information retrieval and reliability assessment. The workflow starts with
identifying anomalous asset price changes using machine learning models trained
with historical pricing data, and retrieving correlated unstructured data from
Twitter using enhanced queries with dynamic search conditions. DeepTrust
extrapolates information reliability from tweet features, traces of generative
language model, argumentation structure, subjectivity and sentiment signals,
and refine a concise collection of credible tweets for market insights. The
framework is evaluated on two self-annotated financial anomalies, i.e., Twitter
and Facebook stock price on 29 and 30 April 2021. The optimal setup outperforms
the baseline classifier by 7.75% and 15.77% on F0.5-scores, and 10.55% and
18.88% on precision, respectively, proving its capability in screening
unreliable information precisely. At the same time, information retrieval and
reliability assessment modules are analyzed individually on their effectiveness
and causes of limitations, with identified subjective and objective factors
that influence the performance. As a collaborative project with Refinitiv, this
framework paves a promising path towards building a scalable commercial
solution that assists traders to reach investment decisions on pricing
anomalies with authenticated knowledge from social media platforms in
real-time.
