Curating Social Media Data

TL;DR

This paper introduces CrowdCorrect, a comprehensive data curation pipeline for social media data, combining automated and crowd-sourced methods to improve data quality for analytics.

Contribution

It presents a novel pipeline that automates feature extraction and integrates crowd-sourcing for data correction, enhancing social media data analysis accuracy.

Findings

Effective automatic feature extraction from social media data.

Successful integration of crowd-sourced corrections.

Improved data quality for social media analytics.

Abstract

Social media platforms have empowered the democratization of the pulse of people in the modern era. Due to its immense popularity and high usage, data published on social media sites (e.g., Twitter, Facebook and Tumblr) is a rich ocean of information. Therefore data-driven analytics of social imprints has become a vital asset for organisations and governments to further improve their products and services. However, due to the dynamic and noisy nature of social media data, performing accurate analysis on raw data is a challenging task. A key requirement is to curate the raw data before fed into analytics pipelines. This curation process transforms the raw data into contextualized data and knowledge. We propose a data curation pipeline, namely CrowdCorrect, to enable analysts cleansing and curating social data and preparing it for reliable analytics. Our pipeline provides an automatic feature extraction from a corpus of social media data using existing in-house tools. Further, we offer a dual-correction mechanism using both automated and crowd-sourced approaches. The implementation of this pipeline also includes a set of tools for automatically creating micro-tasks to facilitate the contribution of crowd users in curating the raw data. For the purposes of this research, we use Twitter as our motivational social media data platform due to its popularity.