geoso

A Python package for collecting and spatio-temporal analysis of social media contents

• GitHub repo: https://github.com/MahdiFarnaghi/geoso
• PyPI: https://pypi.org/project/geoso/
• Documentation: https://MahdiFarnaghi.github.io/geoso
• Free software: MIT license

Introduction

geoso is a Python library, being developed to facilitate collection, cleansing, and spatial and spatio-temporal analysis of social media data.

The vision is that the library provided the possibility to download geo-tagged social media content into a database, e.g., PostgreSQL, preprocess the stored data, retrieve, and analyse the data.

Features

• Twitter
  • Download tweets from Twitter Streaming API and save them into either a database or JSON Lines text files.
  • Import tweets that were from JSON Lines text files into the database.
  • Export tweets to CSV file.
  • Clean tweets text in the database.
  • Retrieve tweets from the database as pandas DataFrame.

Under development features

• Twitter
  • Clean tweets text in the database.
  • Detect tweets that were published by bots.

Scientific publications

If you are using this library, the following scientific publications could be of your interest.

• Farnaghi, M., Ghaemi, Z., & Mansourian, A. (2020). Dynamic Spatio-Temporal Tweet Mining for Event Detection: A Case Study of Hurricane Florence. International Journal of Disaster Risk Science, 11, 378-393.

• Ostermann, F. O. (2021). Linking geosocial sensing with the socio-demographic fabric of smart cities. ISPRS international journal of geo-information, 10(2), 1-22. [52].

• Zahra, K., Imran, M., Ostermann, F. O. (2020). Automatic identification of eyewitness messages on twitter during disasters. Information processing & management 57 (1), 102107

• Ghaemi, Z. & Farnaghi, M. 2019. A Varied Density-based Clustering Approach for Event Detection from Heterogeneous Twitter Data. ISPRS International Journal of Geo-Information, 8 (2).

Credits

• spaCy is used for cleaning texts.
• Tweepy is used to develop Twitter data retrieval functionalities.
• This package was created with Cookiecutter and the giswqs/pypackage project template and instructions from Python Packages book.