# DeepTrust: A Reliable Financial Knowledge Retrieval Framework For Explaining Extreme Pricing Anomalies

arXiv: https://arxiv.org/abs/2203.08144 · HTML: https://mlsanity.com/paper/2203.08144/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 126 references that are in the map.
- First posted on arXiv 11 Mar 2022.

## Builds on (most cited first, 29 of 29)

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- [Fake News Identification on Twitter with Hybrid CNN and RNN Models](https://mlsanity.com/paper/1806.11316/) (2018)
- [TweetCred: Real-Time Credibility Assessment of Content on Twitter](https://mlsanity.com/paper/1405.5490/) (2014)
- [TweetBERT: A Pretrained Language Representation Model for Twitter Text Analysis](https://mlsanity.com/paper/2010.11091/) (2020)
- [Efficient Twitter Sentiment Classification using Subjective Distant Supervision](https://mlsanity.com/paper/1701.03051/) (2017)
- [Event detection in Twitter: A keyword volume approach](https://mlsanity.com/paper/1901.00570/) (2019)
- [Enhancing keyword correlation for event detection in social networks using SVD and k-means: Twitter case study](https://mlsanity.com/paper/1807.09561/) (2018)

## Abstract (arXiv)

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.