1. Twitter Hashtags RMBI4310Spring 2016 Group 14 Cheung Hiu Yan, Debbie20120038 Chow Miu Lam, Carman20121408 Tsang Wing Wah, Denise20124917

2. Terminology 1. Tweet  Individual message within 140 characters 2. Hashtag  A string of characters preceded by the symbol # 3. Follower & Followee  If User A follows User B, then A is a follower of B and B is a followee of A

3. Why Hashtag was invented?

4. Hashtags and followers : An experimental study of the online social network Twitter Eva García Martín

5. Motivations ➢ To spread the information more widely ➢ To help marketing companies to correctly target their customers

6. Objectives ➢ To investigate a correlation between hashtags and change in number of followers

7. Experiment 1. Gathered random users that tweeted with hashtags and without hashtags  Control Group - do not use hashtag  Experimental Group - use hashtag 2. Computed the difference in the number of followers every 30 minutes 3. Collected data for a period of 7 days

8. Experiment Gathered random users Computed the difference in the number of followers Collected data for a period of 7 days Control Group - not use hashtag Experimental Group - use hashtag

9. Evaluation Null hypothesis is rejected Compare the change in number of followers H0: Median of Control Group = Median of Experimental Group H1: Median of Experimental group > Median of Control group Non-parametrical Mann-Whitney U-test Test whether the 2 groups represent different median values

10. Result ➢ Correlation is shown between hashtags and followers ➢ Tweets that contain hashtags are more likely to lead to a higher increase in the number of followers than tweets without hashtags.

11. Result ➢ Tweets with more than two hashtags results in a decrease in the number of followees

12. Future Work ➢ Further investigation on which type of hashtags can attract followers

13. On Analyzing Hashtags in Twitter Paolo Ferragina Francesco Piccinno Roberto Santoro

14. Motivations ➢ Extracting information from hashtags is difficult ➢ Composition is not constrained by any rule ➢ Usually appear in short and poorly written messages ➢ Difficult to analyze with classic IR techniques

15. Objectives Introduce the Hashtag-Entity Graph and proper algorithmsTo solve IR problems formulated on Twitter hashtagsBetter hashtag classification

16. What is Hashtag-Entity Graph? ➢ A weighted labeled graph made up of hashtags and entities drawn from a set of tweets Purple node: hashtag Green node: entity (pages drawn from Wikipedia) Black edge: links two entities which are semantically related Green edge: links a hashtag node iff they co-occur in the same tweet

17. Relatedness ➢ Devise a relatedness function for two hashtags, with an output value ranging [0; 1] 0 semantically unrelated 1 semantically related

18. Relatedness ➢ Expanded Consine Similarity (ExpCosEntity) & Personalized PageRank Relatedness (CosPPR) ➢ If the relatedness obtained from both methods are high (low), then the two hashtags are related (unrelated) ➢ If the output of CosPPR is significantly lower than that of ExpCosEntity, then the two hashtags are weakly related

19. Classification Classifier HE- Graph Wikipedia Category graph 8 Categories

20. Conclusion ➢ Tweets with hashtags ➢ result in higher increase in the number of followers ➢ help companies accurately target their customers in social media campaign ➢ Precise classification algorithm for hashtags ➢ allow further investigation on what types of hashtag could attract followers

21. Q&A