Wesslen, R., Nandu, S., Eltayeby, O., Gallicano, T., Levens, S., Jiang, M., and Shaikh, S. (2018). Bumper Stickers on the Twitter Highway: Analyzing the Speed and Substance of Profile Changes. ICWSM 2018 Poster Paper.
@inproceedings{twitterbumpersticker,
title = {Bumper Stickers on the Twitter Highway: Analyzing the Speed and Substance of Profile Changes},
author = {Wesslen, Ryan and Nandu, Sagar, and Eltayeby, Omar and Gallicano, Tiffany and Levens, Sara and Jiang, Min and Shaikh, Samira},
booktitle = {Proceedings of the 12th International AAAI Conference on Web and Social Media},
series = {ICWSM '18},
year = {2018},
location = {Palo Alto, California}
}
The code is written in R 3.4.3 or higher. Highly recommend using RStudio 1.1.383 or higher.
Open the file twitter-bumper-sticker-icwsm-2018.Rproj.
- Twitter API and tweetscores package code to run the experiment (Rmd / HTML)
- Topic Modeling on profile summaries to get groups (Rmd / HTML)
- Word probabilities (raw and FREX) for labeling topic groups (Rmd / HTML)
- Plots used in the paper (Rmd / HTML)
Due to Twitter's Terms-of-Services, we are not able to provide the experiment data publicly. However, please email rwesslen@uncc.edu if you are interested in obtaining the dataset for research purposes only.
The dataset pulled a 24 hour 1% sample of Tweets (3.4MM tweets) on September 28, 2017. Using that dataset, we identified 2.58MM unique Twitter profile ID's (actor.id). Using the tweetscores package, we then received a snapshot of each Twitter profile over a two week period. Each snapshot (i.e., 2.58MM profiles) took approximately 36 hours, thus yielding 9 snapshots of profiles over a two week period.