The spread of information on Twitter based on sentimentncuwm/21stAnnual/presentation-library/... ·...

The spread of information on Twitter based onsentiment

Haley Knox

Eastern Connecticut State University

Mentors: Dr. Garrett Dancik and Dr. Megan Heenehan

January 26, 2019

Haley Knox (Eastern CT State University) Twitter sentiment analysis January 26, 2019 1 / 22

Twitter

A tweet can be spread by ‘liking’, replying, quoting or retweeting.Some stories spread across the world almost instantaneously.One Direction star Harry Styles tweeted about his band’s breakup andin fifteen seconds, it moved from the United States to just about everycorner of the planet [1].We look to discover features of what makes information spread onTwitter.


Sentiment

We determine if sentiment impacts whether or not a user retweets.Are positive or negative tweets more likely to be retweeted?Is the number of retweets correlated with sentiment?

We classify the sentiment of a tweet as positive, negative or neutraland analyze how these tweets spread.We use the package sentimentr in R to calculate the sentiment oftweets.


Examples

Negative sentiment:“Because unlike you lazy lot they won’t sigh and go “it is well” afterbeing owed salaries. They’ll fight. Throw chairs. Threatenimpeachment. Scheme. They are not lazy nor foolish like we thinkthem. They know their strengths and apply it accordingly.”

-1.668267

Positive sentiment:“So nice getting to meet the brilliant Eddie Redmayne! Absolutely loveFantastic Beasts: The Crimes Of Grindelwald! #FantasticBeasts#ProtectTheSecrets #BeFantastic”

1.4546


Network analysis

We build networks for 20 positive and 20 negative tweets.We use graph theory measures to see if there are any measurabledifferences between the networks of positive and negative tweets.


How we build our networks

Twitter doesn’t accurately show whom someone retweeted.According to Twitter, everyone retweets the author of the tweet, sothe retweet network is a star graph.

Retweet network based on Twitter’s information. This is a star graph.


Our networks

1 We consider all of the retweeters of a tweet.2 We get the ‘friends’ of all the retweeters.3 If someone is a friend of a retweeter and is also a retweeter, then we

form an edge between them.

This shows where a user saw the tweet from and who they actuallyretweeted.


The data

We collected 12,000 random tweets at 12pm and 10pm every day fortwo weeks.We built networks for one positive and negative tweet from eachsample that had 50-100 retweets and a sentiment score > |0.9|.

0

5000

10000

15000

Sund

ay

Mon

day

Tues

day

Wed

nesd

ay

Thur

sday

Frid

ay

Satu

rday

Day

Cou

nt

Sentiment

Negative

Positive

Tweets sampled each day


0

20000

40000

60000

−1 0 1Sentiment

Histogram for sentiment of all data

The distribution of sentiment for all of our samples.

Positive Negative Neutral98,038 61,960 49,039

The number of tweets in our data set by sentiment.


Saturday

Wednesday Thursday Friday

Sunday Monday Tuesday

0

5

10

15

0

5

10

15

0

5

10

15

Sentiment

log(

retw

eet c

ount

+ 1

)

Retweet count by sentiment for each day

The number of retweets on the log scale by sentiment for each day of the week.We consider tweets with or without any retweets.


0

5

10

15

Sentiment

log(

retw

eet c

ount

+ 1

)Retweet count by sentiment

The number of retweets on the log scale by sentiment for our entire dataset. Weconsider tweets with or without any retweets.


Likelihood of retweet based on sentiment

We previously determined that the greater the polarity of the tweet,the more retweets that tweet will receive.Now we look into if a tweet is more likely to be retweeted based on ifit is positive or negative.Recall that there are more positive tweets in our data set thannegative tweets, so we will use proportions.


0.00

0.25

0.50

0.75

1.00

nega

tive

neut

ral

posi

tive

Sentiment

Pro

port

ion Retweet count > 1000

No

Yes

Retweet count > 1000 by sentiment

The proportion of negative, neutral, and positive tweets that are retweeted morethan 1000 times.


Results

15% of negative tweets are retweeted more than 1000 times.Only 12.3% of positive tweets are retweeted more than 1000 times.This difference is statistically significant (Fisher Test results in ap-value of 0.0004997).Therefore, negative tweets are more likely than positive tweets to beretweeted more than 1000 times.The same is true for retweet count > 1 and retweet count > 100.


Network analysis

We calculate the Pearson correlation between sentiment score and:group betweennessgroup degreemodularitygroup closenessthe number of communitiesdensityaverage clustering coefficient


r(pos) p-value(pos)

r(neg) p-value(neg)

Difference p-value(diff)

Betweenness 0.1771 0.4821 0.0797 0.761 0.0973 0.7898Degree 0.0374 0.883 0.3379 0.1846 -0.3006 0.3976Modularity 0.1368 0.5884 0.3366 0.01186 -0.1998 0.5672Closeness -0.1364 0.5894 -0.5944 0.1865 0.458 0.1409Communities 0.1995 0.4273 -0.3865 0.1254 0.5861 0.1007Density -0.1042 0.6807 0.2824 0.2721 -0.3866 0.288Avg. C4 -0.2399 0.3375 0.3732 0.1401 -0.6132 0.0866

Pearson correlation of graph theoretic measures compared to sentiment score.


0.000.050.100.150.200.25

negative positiveSentimentG

roup

bet

wee

nnes

s

Betweenness

0.000.250.500.75

negative positiveSentiment

Mod

ular

ity

Modularity

48

12


Com

mun

ities

Communities

0.250.500.751.00


Gro

up d

egre

e Degree

0.00.20.40.60.8

negative positiveSentimentA

vg. c

lust

erin

g co

eff.

Avg. clustering coefficient

0.000

0.005

0.010

negative positiveSentiment G

roup

clo

sene

ss Closeness

Comparing positive and negative sentiments among our chosen network measures.


Network example

Network for a tweet by @AubreyKMiller. The size of the vertices represents thenumber of followers of that user. The lighter the color, the more friends that userhas.


Network example

The tweet is by @AubreyKMiller: "So nice getting to meet the brilliantEddie Redmayne! Absolutely love Fantastic Beasts: The Crimes OfGrindelwald! #FantasticBeasts #ProtectTheSecrets #BeFantastic."Fantastic Beasts is a movie starring actor Eddie Redmayne.While Aubrey is the original author of the tweet, she is not the mostimportant.@AubreyKMiller only has a degree of 11.@FantasticBeasts has a degree of 56.Our construction of a network is much different than that of a stargraph with @AubreyKMiller as the center vertex.


Conclusion

There are more positive tweets than negative tweets.Negative tweets are slightly more likely to be retweeted than positivetweets.The larger the absolute value of the sentiment score (i.e. the greaterthe emotional impact), the more retweets the tweet is likely to have.


Acknowledgments

I would like to thank my mentors Dr. Dancik and Dr. Heenehan for theirhelp and constant support throughout this research project and theNCUWM committee that put together this wonderful conference.


References

Beres, Damon. (2016) Watch The Amazing Way Information SpreadsOn Twitter. Huffington Post, Mar 20, 2016.https://www.huffingtonpost.com/entry/how-twitter-works_us_56ec6480e4b084c672204d74

A. K. Jose, N. Bhatia, and S. Krishna. (2010) “Twitter SentimentAnalysis”. National Institute of Technology Calicut.

Sarlan, A., Nadam, C., and Basri, S. (2014) Twitter sentimentanalysis. 212-216. doi:10.1109/ICIMU.2014.7066632.

https://www.rdocumentation.org/packages/sentimentr. pages745-750.

Barabási, A.-L. and Pósfai, M. (2016) Network science. Cambridge:Cambridge University Press.


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