Predicting Political Positions With Twitter By: Austin Smith November 8, 2016

Overview Introduction Related Works Methodology

2016 Election One of the most unpredictable elections to date. How do politicians target voters? Republicans vs. Democrats

Twitter A way to get ideas broadcasted to the rest of the world. Tweet’s are a way for people to express themselves and broadcast their thoughts publically. Can politicians use tweets to exploit someone’s political orientation?

Using Hashtags to Make Predictions Inaccurate Are they using hashtag in a positive or negative way? Accurate with politicians, but not with “normal” users.

Generalized Emotion Proven to be more accurate. Are there differences in generalized emotion between democrats and republicans? Yes!

Related Work Pt. 1 Twitter Language Use Reflect Psychological Differences Between Democrats and Republicans published by Karolina Sylwester and Matthew Purver Analyzes the different ways liberals and conservatives interact with Twitter. Tend to mention the other parties candidates more frequently than their own.

Republican’s Most Used Words Tend to use more “conservative” words. God, Psalm, America, American, Liberal, Country, Border. Community words: Us, We.

Democrats Most Used Words Emotionally expressive words Happi, excited, awesome, like, feel, amaz Use more swear words Self identifying words such as I and Me.

Related Word Pt. 2    Predicting Twitter User Demographics using Distant Supervision from Website Traffic Data by Aron Culotta, Nirmal Kumar Ravi, and Jennifer Cutler Regression models in order to predict seven demographics from Twitter users. Analyzing the data based on who they follow and the context of their tweets Found that the stronger web presence one has, the easier it is to predict their demographic.

Related Work Pt. 3    Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment by Andranik Tumasjan, Timm O. Sprenger, Philipp G. Sandner, and Isabell M. Welpe Aims to find if Twitter can be used as a form of political deliberation. Predict elections just by looking at the number of messages mentioning a political party. Fell within 1.65% mean absolute error.

Related Work Pt. 4    Classifying Political Orientation on Twitter: It’s Not Easy! By Raviv Cohen and Derek Ruths Aims to prove that data retrieved from Twitter, in order to make predictions about a user’s political stance, has proven to not be as accurate when analyzing hashtags. 60% accuracy when using hashtags. More accurate to analyze behavior not hashtags

Methodology Will be using word frequencies of known Republicans and Democrats to predict political orientation. Why known Democrats and Republicans? In order to find out if the predictions made are accurate or inaccurate. Quality data, not quantity.

Pulling the Data Compiled a list of 50 known Democrats and 50 known Republican celebrities. No politicians used! Obtain individual word frequencies of their last 1,000 tweets.

Tweetails Web based software which generates word frequency graphics Analyzes a twitter users last 1,000 tweets Does not include 1,000 most common English words

Word Frequencies of Entire Groups Take the word frequencies from the 50 Democrats used and 50 Republicans used. Separate the word frequencies into two separate lists.

Conclusion Predicting a Twitter user’s political orientation based off of their tweets is possible. Predictions can be made with around 90% accuracy. Found that democrats were easier to predict then republicans. Word frequencies of entire groups provides more accurate results.

Questions?