Prediction of Influencers from

Word UseChan Shing Hei

• Detecting influential users before they show observable signals of influence

• Method:psycholinguistic category scores from word usage

• Twitter Databuilt predictive models of influence from such category based features

Introduction

Measuring Influence

• measure influence score

the average number of retweets generated from a user's tweets

Dataset• Twitter's streaming API

• Nov 1, 2013 to Nov 14, 2013

• randomly sample 1000 users

• Tweets from last one month (Oct 2013)

• historical tweets (max 200)

• average influence score: 0.213 SD: 0.098

Psycholinguistic Analysis from text• How to measure word use ?

users’ historical tweets with the Linguistic Inquiry and Word Count (LIWC) 2001 dictionary

• How to computed his/her LIWC based scores?

in each category as the ratio of the number of occurrences of words in that category in one’s tweets and the total number of words in his/her tweets

Influence and Word Use

Finding from analysis

• LIWC category that negatively correlated with influence score:

Negative emotion

physical states

inhibition

• LIWC category that positively correlated with influence score:

more interactive

positive feelings or emotion

determination and desires for the future

Finding from previous analysis

Prediction Models

• Using Weka

• regression analysis and a classification influence score

• Evaluate the performance

Regression analysis

• linear regressions predict influence score using LIWC measures

Classification study 1

• supervised binary machine learning algorithms

• divide influence scores into 10 equal sized bins, and trained supervised classifiers with 10 classes

Classification study 2

Suggestion

• Adding more criteria for measure influence

• Other social media

• Real application – political campaigns

Conclusion

• Correlations of word usage with influence behavior

• discovers a set of psycholinguistic categories

• identify users early to be an influencer