Combining Automatic and Manual Approaches

Abstract

In this paper, we present a framework that combines automatic and manual approaches to discover themes in disaster-related tweets. As case study, we decided to focus on tweets related to typhoon Haiyan, which caused billions of dollars in damages. We collected tweets from November 2013 to March 2014 and used the local typhoon name "Yolanda" as the filter. Data association was used to expand the tweet set and k-means clustering was then applied. Clusters with high number of instances were subjected to open coding for labeling. The Silhouette indices ranged from 0.27 to 0.50. Analyses reveal that the use of automated Natural Language Processing (NLP) approach has the potential to deal with huge volumes of tweets by clustering frequently occurring words and phrases. This complements the manual approach to surface themes from a more manageable set of tweet pool, allowing for a more nuanced analysis of tweets from a human expert. As application, the themes identified during open coding were used as labels to train a classifier system. Future work could explore on using topic models and focusing on specific content or issues, such as natural calamities and citizen's participation in addressing these.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 6 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.