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TBAM: Towards An Agent-Based Model to Enrich Twitter Data

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arxiv 2302.00128 v1 pith:SD322AJJ submitted 2023-01-31 cs.SI

Usman Anjum , Vladimir Zadorozhny , Prashant Krishnamurthy This is my paper · ORCID
classification cs.SI
keywords datatwitterbehaviormodelagent-basedmicrobloggingtbamevent
verification ladder T0 review T1 audit T2 compute T3 formal

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Twitter (one example of microblogging) is widely being used by researchers to understand human behavior, specifically how people behave when a significant event occurs and how it changes user microblogging patterns. The changing microblogging behavior can reveal patterns that can help in detecting real-world events. However, the Twitter data that is available has limitations, such as, it is incomplete and noisy and the samples are irregular. In this paper we create a model, called Twitter Behavior Agent-Based Model (TBAM) to simulate Twitter pattern and behavior using Agent-Based Modeling (ABM). The generated data from ABM simulations can be used in place or to complement the real-world data toward improving the accuracy of event detection. We confirm the validity of our model by finding the cross-correlation between the real data collected from Twitter and the data generated using TBAM.

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  1. Towards Modeling Data Quality and Machine Learning Model Performance

    cs.LG 2024-12 reject novelty 3.0 of 10

    The paper repackages signal-to-noise ratio as a data-quality metric, DDR, and uses controlled synthetic noise to draw accuracy-DDR curves and define a trustworthiness portfolio.

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