Twitter trolls strategy to disrupt democracy.
Social media went from exciting to being ingrained in our societies and its influence exceeded the personal sphere to attain even our legal framework and political institutions. The question of regulating social networks is currently a hot topic especially with the rise of the concept of cyber warfare and what that entails in terms of disruption and propaganda. It is therefore very important to study how social media can be used to create narratives and tip the scales into one way or the other. In particular, the Internet Research Agency, a Russian troll factory was accused of interfering in multiple foreign political processes like the the 2016 US presidential elections. We dispose of a dataset of roughly 3 millions tweets spanned between 2012 and 2018 related to accounts of this agency. The aim is to understand what these tweets focus on and how do they adapt with respect to major political and politically divisive events.
Through this project we want to address the following questions:
How does the troll tweets activity evolve with respect to the events?
What are the most discussed Russian topics and do they match the timeline of events?
What's the difference between the Russian and Iranian trolls in terms of topics, hashtags etc ?
Can we classify the unlabeled Russian trolls data based on the previous labeled one ?
Starting from the aforementioned questions we might add new evaluations and studies according to the insights we will get from the data.
The dataset we are using for this project is composed of 2,973,371 tweets from 2,848 Twitter handles distributed in 9 csv files. The twitter handles are linked to the Internet Research Agency, a Russian troll factory accused of interfering in the 2016 US presidential elections. The tweets in the dataset were sent between February 2012 and May 2018 with the vast majority posted from 2015 through 2017.
For each tweet, we have the following attributes:
| Header | Definition |
|---|---|
external_author_id |
An author account ID from Twitter |
author |
The handle sending the tweet |
content |
The text of the tweet |
region |
A region classification |
language |
The language of the tweet |
publish_date |
The date and time the tweet was sent |
harvested_date |
The date and time the tweet was collected by Social Studio |
following |
The number of accounts the handle was following at the time of the tweet |
followers |
The number of followers the handle had at the time of the tweet |
updates |
The number of “update actions” on the account that authored the tweet, including tweets, retweets and likes |
post_type |
Indicates if the tweet was a retweet or a quote-tweet |
account_type |
Specific account theme, as coded by Linvill and Warren |
retweet |
A binary indicator of whether or not the tweet is a retweet |
account_category |
General account theme, as coded by Linvill and Warren |
new_june_2018 |
A binary indicator of whether the handle was newly listed in June 2018 |
We are focused on the tweets in English. The periods of time we're interested in coincide with major political and major politically devasive events.
We also used the troll data released by Twitter in October consisting of roughly 10 million tweets traced back to the IRA and the Iranian govermennt. Lastly, we gathered 1.8 million tweets from the twitter internet archive on the hdfs. We filtered 150 Gb of tweets from August 2017 based on a small list of generic politically related words.
Steps up to milestone 2 are:
Explore data and parse it.
Filter and preprocess data.
Perform descriptive analysis on the dataset.
Check the timeline of major troll tweets activity and align it with major political and social events.
Later we will focus more on the following:
Topic extraction, modeling and matching with subsequent events.
Sentiment analysis on a number of topics.
Search for correlation between topics, language and perception inferred by the tweets.
Add here some questions you have for us, in general or project-specific.
In milestone, we did the following: Descriptive analysis of the data. Investigation of the monthly, daily and hourly IRA tweet activity. Topic modeling initiation. Polarity and sentiment analysis initiation.
Data story has the following scheme:
Russian trolls data exploration
Tweets activity
Topic extraction before, during and after the compaign for the FiveThirtyEight Russian trolls' data, Iranian trolls' data and general public's data
Classification of troll category
Ahmed: Data story, Exploratory analysis, tweets activity, data fetching. Jessica: Topic extraction and discussion. Neeraj: Running classifier, hashtags-category matching, cleaning notebook.
Link to data story: https://ada-asjl.github.io/ Repo data story: https://github.com/ADA-ASJL/ADA-ASJL.github.io