# Extracting and Analyzing Context Information in User-Support Conversations on Twitter

## TL;DR

This paper presents a method to extract key context information from informal Twitter feedback about mobile apps, enabling automated, structured bug reporting to improve developer support efficiency.

## Contribution

It introduces a simple, accurate approach for extracting context data from unstructured social media feedback, enhancing automated bug report generation.

## Key findings

- Achieved 81-99% precision in extracting context information
- Achieved 86-98% recall across different context items
- Demonstrated effectiveness on tweets from popular app support accounts

## Abstract

While many apps include built-in options to report bugs or request features, users still provide an increasing amount of feedback via social media, like Twitter. Compared to traditional issue trackers, the reporting process in social media is unstructured and the feedback often lacks basic context information, such as the app version or the device concerned when experiencing the issue. To make this feedback actionable to developers, support teams engage in recurring, effortful conversations with app users to clarify missing context items. This paper introduces a simple approach that accurately extracts basic context information from unstructured, informal user feedback on mobile apps, including the platform, device, app version, and system version. Evaluated against a truthset of 3014 tweets from official Twitter support accounts of the 3 popular apps Netflix, Snapchat, and Spotify, our approach achieved precisions from 81% to 99% and recalls from 86% to 98% for the different context item types. Combined with a chatbot that automatically requests missing context items from reporting users, our approach aims at auto-populating issue trackers with structured bug reports.