What’s in a Country Name – Twitter Hashtag Analysis of #singapore

presented by

Aravind Sesagiri Raamkumar

PhD Student, Division of Information Studies

WKWSCI, NTU

Agenda

• Introduction• Literature Review• Research Objectives• Results & Discussion• Conclusion & Future Works

Introduction• Twitter introduced in 2006, popularized the

concept of Micro-blogging with 140 character limit posts called as Tweets

• Main Activities: Tweeting (Posting Multimedia Content), Personal Messaging, Retweeting, and Real-time Information Searching

• Hashtags are keywords starting with symbol “#” used for Content Categorization and Participation in Conversations (popular examples are #FIFAWorldCup, #OWS)

Introduction• Why study hashtags?

– To understand the growth and decline of hashtags

– Do hashtags really help in content categorization?

– To provide inputs for hashtag recommendation algorithms

– To understand the dynamics around hashtags in basic usage and community participation

Literature Review• Hashtag Studies

– Studies have either taken an holistic approach [7] or event specific approach (e.g., US Presidential elections) [3]

– Pöschko analyzed tweets containing hashtags for developing five hashtag classes – Geolocation, Person, Organization, Event and Category [7]

– Bruns et al. showed that hashtags are formed through pre-planning, quickly reached consensus or through protracted debate [8]

– Yang et al. validated the dual purpose of hashtags in content grouping and community participation. A machine learning based hashtag usage prediction algorithm proposed [9]

Where to focus?• Hashtag Studies

– Prior hashtag studies have concentrated on major events and not commonplace hashtags used on daily basis such as country names (e.g.,location based hashtags)

• Tweet Classification– Tweet classification approaches have not used

hashtags as frame of reference

Why do users make use of the hashtag

#singapore in their tweets?

What are the categories that represent the tweets?

Manual & Automatic Tweet Classification

What are the communication patterns between users using #singapore?

Social Network Analysis

Does the provenance data of tweets provide any new insights?

Content Analysis

Research Questions & Methods

Overarching Research Question

Individual Research Questions

Methods

Methods• Manual Tweet Classification

– Performed by three coders for 500 tweets selected from two days of the total period.

– Hashtag #singapore used as the frame of reference

– Categories identified by the first coder, passed over to the other two coders for classification

• Automatic Tweet Classification– The statistical programming language R with the machine

learning library RTextTools used for the exercise

– Maximum Entropy shortlisted as the machine learning text classificatication algorithm

Methods (contd…)• Social Network Analysis

• Exploring communication patterns between user accounts who have posted tweets with #singapore in the sample set

Sample Tweet: @Stanbridge @NatGeo We hope you enjoyed your time at #50GreatestPhotos and we look forward to your next visit! #singapore

• Gephi tool used for building graphs

Data Collection• TweetArchivist was used to extract live data from

Twitter for the hashtag #singapore for the period Aug 20th to Sep 1st 2013

Date Tweet Count Day of weekAug-20 1360 TueAug-21 2305 WedAug-22 2776 ThurAug-23 3780 FriAug-24 2753 SatAug-25 2524 SunAug-26 2387 MonAug-27 3045 TueAug-28 2635 WedAug-29 3298 ThurAug-30 3574 FriAug-31 2930 SatSep-01 2888 SunSep-02 3009 MonSep-03 2644 TueSep-04 2676 WedSep-05 2554 Thur

Results & Discussion

Results & Discussion – Tweet Classification• N = 5140 (28.9%)• e.g.,The Strike That Rattled #Singapore: A WSJ Investigation. The first of a 5-part series this week. http://t.co/nAKrimR4OG

#China #migrantsLocal Events and News (LEN)

• N = 4973 (27.9%)• e.g.,Sky walk , #singapore #trip #nature @ Gardens By The Bay http://t.co/Uhxxknsn1A

Current Location and Landmarks (CLL)

• N = 2822 (15.9%)• e.g.,Almost Chinese hates Commie Govt http://t.co/YVH8Ni9su4 #Taiwan #Turkey #India #Thailand #Singapore #Brazil

#Vietnam

Asia Related and Unrelated topics (ARU)

• N = 2142 (12.0%)• e.g.,Have a beautiful week ahead, bellas! #bellabox #singapore #beauty http://t.co/tVvypOCa9L

Commercial Deals (CD)

• N = 1302 (7.3%)• Had a fantastic time in #Singapore. Should be home in 14 hours. #ChangiAirport

Tourism and Travel Related (TTR)

• N = 1214 (6.8%)• e.g.Morning #SGFBloggers. Lets make it a positive and productive week!positive #singapore #love #quotes

http://t.co/cBR8sw3Tpt,

National Identity and Group Reference (NGR)

• N = 205 (1.1%)• e.g.,In what should've been a normal 30mins ride to work, I'm now 30 mins late for the meeting #Traffic #singapore

Personal Events and Rants (PER)

Results & Discussion – Social Network AnalysisDirected graph built with ‘user mentions’ data

Popular network actors are New agencies and Celebrities and Commercial Bodies

Retweeting appears to be a major activity

Results & Discussion – Provenance Data

Source Tweet Count PercentageInstagram 3206 18.01%

web 1844 10.36%Twitter for iPhone 1629 9.15%

Twitter for Android 1432 8.05%dlvr.it 1212 6.81%

twitterfeed 891 5.01%RoundTeam 686 3.85%

Tweet Old Post 598 3.36%HootSuite 555 3.12%

TweetAdder v4 547 3.07%

URL Presence Tweet Count PercentageNot Present 5256 29.53%

Present 12542 70.47%

Instagram is the most used source Higher % of Tweets (18%) with images of Singapore

landmarks

Information or Content Sharing is the most

prevalent activity with 70% of tweets containing

URLs

Retweeting is not the most major activity (31% of

tweets) at the detailed levelType Count PercentageNormal Tweet 12165 68.35%Retweet 5633 31.65%

Conclusion• Seven themes/categories were identified as a part of a

tweet classification exercise.

• #singapore is prominent in tweets about local events, local news, users’ current location and landmark related information sharing.

• Users who share content from Instagram make use of the hashtag in a more prominent way.

• News agencies, commercial bodies and celebrities make use of the hashtag more than common individuals.

Future Work • Similar classification exercise will be carried out for

different country names so as to validate the identified categories.

– #singapore data from Facebook and Google plus to be studied for cross-verification

– Intent to put forth a classification scheme for location based hashtag studies

– Provide inputs to hashtag recommendation algorithms

Thank You

Selective Bibliography[1] Cheong, M., & Lee, V. (2010). Dissecting Twitter: A Review on Current Microblogging Research and Lessongs from Related Fields. From Sociology to Computing in Social Networks (pp. 343–362). Springer Vienna.[2] Sriram, B., Fuhry, D., Demir, E., Ferhatosmanoglu, H., & Demirbas, M. (2010). Short text classification in twitter to improve information filtering. Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval - SIGIR  ’10 (pp. 841–842). New York, New York, USA: ACM Press. doi:10.1145/1835449.1835643[3] Lin, Y., Margolin, D., Keegan, B., Baronchelli, A., & Lazer, D. (2012). # Bigbirds Never Die : Understanding Social Dynamics of Emergent Hashtags.[4] Abel, F., Herder, E., Houben, G., & Henze, N. (2011). Cross-system User Modeling and Personalization on the Social Web. User Modeling and User-Adapted Interaction, 22(3), 1–42.[5] Godin, F., Schrauwen, B., & Walle, R. Van De. (2013). Using Topic Models for Twitter Hashtag Recommendation. Proceedings of the 22nd international conference on World Wide Web companion (pp. 593–596).[6] Magnani, M., Montesi, D., Nunziante, G., & Rossi, L. (2011). Conversation Retrieval from Twitter. Proceedings of the 33rd European Conference on IR Research, ECIR 2011 (pp. 780–783).[7] Pöschko, J. (2011). Exploring Twitter Hashtags (pp. 1–12). Retrieved from http://arxiv.org/abs/1111.6553[8] Bruns, A., & Burgess, J. (2011). The Use of Twitter Hashtags in the Formation of Ad Hoc Publics (pp. 25–27).[9] Yang, L., Sun, T., Zhang, M., & Mei, Q. (2012). We Know What @ You # Tag : Does the Dual Role Affect Hashtag Adoption ? Proceedings of the 21st international conference on World Wide Web (pp. 261–270).[10] Zangerle, E., & Gassler, W. (2011). Recommending # -Tags in Twitter. Proceedings of the Workshop on Semantic Adaptive Social Web (SASWeb 2011) (pp. 67–78).[11] Li, T., & Wu, Y. (2011). Twitter Hash Tag Prediction Algorithm. ICOMP’11-The 2011 International Conference on Internet Computing.[12] Kywe, S. M. (2012). On Recommending Hashtags in Twitter Networks. Social Informatics, 7710, 337–350.

Appendix Java et al (2007) Jansen et al (2009) Honeycutt & Herring (2009) Pear Analytics (2009) Horn (2010)

Conversations Info seeking About addressee Mainstream NewsC1: News,

Events, Company

URL sharing Info providing Advertise Spam C2: Factual, Opionated

News reporting Comment/Sentiment Exhort Self-promotion of businesses

Daily chatter Info for others BabbleInfo for self Conversations

Meta-commentary Pass-along messages (retweets)

Media useExpress opinion

Other's experienceSelf experience

Solicit info

Other misc

Sriram et al (2010) Dann (2010) Sandra et al (2010) Naaman et al (2010) Rosa (2011)

News Conversational Movies Info sharing News

Opinions Pass along Books Self promotion Sports

Deals News Music Opinions/Complaints Science & Technology

Events Status Apps Statements & Random Thoughts

Entertainment

Private Messages Phatic Games Me now Money/Business

Spam Question to followers Just for Fun

Presence Maintenance

Anecdote

Table 2.2 (a) Classification schemes from previous twitter studies

Table 2.2 (b) Classification schemes from previous twitter studies