Online Digital Investigative Journalism using SociaLens
Hasan M. Jamil, Sajratul Y. Rubaiat

TL;DR
SociaLens is an autonomous AI-powered tool that enhances investigative journalism by enabling data collection, analysis, and report generation from online sources without coding expertise, leveraging ML, LLMs, and big data techniques.
Contribution
The paper introduces SociaLens, a versatile, autonomous investigative journalism system integrating ML, LLMs, and big data search techniques, capable of data extraction, analysis, and report generation.
Findings
Successfully demonstrated on rape incident case study
Enables journalists to gain insights without coding skills
Capable of generating textual and visual reports
Abstract
Media companies witnessed a significant transformation with the rise of the internet, bigdata, machine learning (ML) and AI. Recent emergence of large language models (LLM) have added another aspect to this transformation. Researchers believe that with the help of these technologies, investigative digital journalism will enter a new era. Using a smart set of data gathering and analysis tools, journalists will be able to create data driven contents and insights in unprecedented ways. In this paper, we introduce a versatile and autonomous investigative journalism tool, called {\em SociaLens}, for identifying and extracting query specific data from online sources, responding to probing queries and drawing conclusions entailed by large volumes of data using ML analytics fully autonomously. We envision its use in investigative journalism, law enforcement and social policy planning. The…
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Taxonomy
TopicsDigital and Cyber Forensics
MethodsSparse Evolutionary Training
