Pith. sign in

REVIEW

Why Are You More Engaged? Predicting Social Engagement from Word Use

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1402.6690 v1 pith:MU2RY6KH submitted 2014-02-26 cs.SI cs.CLcs.CY

classification cs.SIcs.CLcs.CY
keywords behaviorsengagementpsycholinguisticsocialtwitterwordcategorydifferent
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We present a study to analyze how word use can predict social engagement behaviors such as replies and retweets in Twitter. We compute psycholinguistic category scores from word usage, and investigate how people with different scores exhibited different reply and retweet behaviors on Twitter. We also found psycholinguistic categories that show significant correlations with such social engagement behaviors. In addition, we have built predictive models of replies and retweets from such psycholinguistic category based features. Our experiments using a real world dataset collected from Twitter validates that such predictions can be done with reasonable accuracy.

Discussion (0). Continue with ORCID to comment.

Pith tools