Twitter Permeability to financial events: an experiment towards a model for sensing irregularities

ArXiv ID: 2312.11530 “View on arXiv”

Authors: Unknown

Abstract

There is a general consensus of the good sensing and novelty characteristics of Twitter as an information media for the complex financial market. This paper investigates the permeability of Twittersphere, the total universe of Twitter users and their habits, towards relevant events in the financial market. Analysis shows that a general purpose social media is permeable to financial-specific events and establishes Twitter as a relevant feeder for taking decisions regarding the financial market and event fraudulent activities in that market. However, the provenance of contributions, their different levels of credibility and quality and even the purpose or intention behind them should to be considered and carefully contemplated if Twitter is used as a single source for decision taking. With the overall aim of this research, to deploy an architecture for real-time monitoring of irregularities in the financial market, this paper conducts a series of experiments on the level of permeability and the permeable features of Twitter in the event of one of these irregularities. To be precise, Twitter data is collected concerning an event comprising of a specific financial action on the 27th January 2017:{"~ “}the announcement about the merge of two companies Tesco PLC and Booker Group PLC, listed in the main market of the London Stock Exchange (LSE), to create the UK’s Leading Food Business. The experiment attempts to answer five key research questions which aim to characterize the features of Twitter permeability to the financial market. The experimental results confirm that a far-impacting financial event, such as the merger considered, caused apparent disturbances in all the features considered, that is, information volume, content and sentiment as well as geographical provenance. Analysis shows that despite, Twitter not being a specific financial forum, it is permeable to financial events.

Keywords: sentiment analysis, information permeability, event detection, social media mining, financial irregularities, Equities

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper relies on statistical analysis of social media features (volume, sentiment) but lacks complex derivations, while it includes a specific case study with collected Twitter data, addressing real-world data collection and event analysis.

  flowchart TD
    A["Research Goal: Assess Twitter's Permeability<br>to Financial Market Irregularities"] --> B["Data Collection<br>Twitter Data on Tesco-Booker Merger<br>Jan 27, 2017"]
    B --> C["Data Processing & Feature Extraction<br>Volume, Content, Sentiment, Geolocation"]
    C --> D{"Computational Analysis<br>Quantifying Permeability"}
    D --> E["Key Findings & Outcomes"]
    E --> F["Twitter is Permeable<br>to Financial Events"]
    E --> G["Impacts Detected in<br>Volume, Sentiment & Content"]
    E --> H["Architecture Validated for<br>Real-time Irregularity Monitoring"]