1. Topical Authority Detection and Sentiment Analysis on Top Influencers
Machine Learning with Large Datasets
Course Project
(under the guidance of Prof. William W. Cohen)
Team Members: Manuel, Shubham and Soumya

2. Outline Introduction Related Work Problem Statement Methodology
Results
Evaluation plan
Conclusion

3. Introduction
Topical authority detection in social networks is an active research area
Important for recommending relevant feed to users interested in certain topics
Challenges -
Results should not be overly biased towards:
popular authors (such as celebrities)
generic authorities (such as news channels)
Relatively new users, who may not exist prior to an event, but post dedicatedly on the topic, should also be considered

4. Related Work
TwitterRank [2]: Authority Detection in Twitter using the idea of PageRank
Leverages topical similarity and link structure between users
Fails to filter out spammers, or celebrities who are not always influential
Meeyoung Cha et. al. [3] find that popular users who have high in- degree are not necessarily influential in terms of spawning retweets or mentions
Aditya Pal et. al. [5] (considered as the baseline):
Use clustering to identify influential vs. non-influential users on Twitter
Rank users in the influential cluster, considering various important features

5. Problem Statement Aim:
Perform authority detection on a collection of topics in Twitter for a time window
Sentiment analysis to determine the influence of top users tweeting on specific topics on their respective communities
Period: June 6th 2010 to June 10th 2010
Topics:
Oil Spill
iPhone
World Cup

6. Methodology - User Metrics
M = Mentions
M1: Number of mentions of other users by the author
M2: Number of unique users mentioned by the author
M3: Number of mentions by others of the author
M4: Number of unique users mentioning the author
G = Graph Characteristics (restricted by the availability of data)
G1: Number of topically active followers
G2: Number of topically active friends
G3: Number of followers tweeting on topic after the author
G4: Number of friends tweeting on topic before the author
OT1: Number of original tweets
OT2: Number of links shared
OT3: Self-similarity score
OT4: Number of keyword hashtags used
CT = Conversational tweets
CT1: Number of conversational tweets
CT2: Tweets where conversation is initiated by the author
RT = Repeated tweets
RT1: Number of retweets of others’ tweets
RT2: Number of unique tweets retweeted by other users
RT3: Number of unique users who retweeted author’s tweets

7. Methodology - Features Extracted
Topic Signal (TS)
Signal Strength (SS)
Non-Chat Signal (NCS)
Retweet Impact (RI) - modified
Mention Impact (MI)
Information Diffusion (ID)
Network Score (NS)
URL Impact (UI)

8. Methodology - Features Formulae

9. Methodology - Steps
Data in Twitter API format -> User Metrics MapReduce (using Hadoop on AWS)
Src-follows-Dest edge-list -> Adjacency Lists
User Metrics and Adjacency Lists -> Features
Features -> Clusters -> Influential Cluster Using Gaussian Mixture Model and Expectation Maximization
Influential Cluster -> Top 20 Influencers Using Gaussian Ranking
Sentiment Analysis and Visualization
Using Liu Hu Lexicon and Gephi

10. Results - Authority Detection
Normalized
Not Normalized
: sandiebanandie : LATenvironment : latimesgreen : dbiello : mrt : BPOilSpill : NWF : climateprogress : ByronYork : Oil_Spill_News : SwampSchool : BrentSpiner : TPM : USGulfOilSpill : kate_sheppard : Fertic : GulfOilCleanup : msnbcvideo : alabamainsider 9848: jimmybuffett
: LATenvironment : BPOilSpill : NWF
: dbiello
: GulfOilCleanup : sandiebanandie : NOLAnews : BoycottBP : Alyssa_Milano
: USGulfOilSpill : guardianeco : climateprogress
: TIME
: ByronYork
: TelegraphNews
:washingtonpost
: mrt7384
:BPOilNews
:greenforyou
: HuffingtonPost

11. Results - Sentiment Analysis
Dbeillo Negative Sentiment Influence
LATenvironment Neutral Sentiment Influence

12. Evaluation - Clustering, Ranking and Authority
We randomly sample users from the “good” and “bad” clusters to ask people how relevant the tweets are for the topic.
Using the assigned rank (1 to 5) of the users from the top k Twitter users in our ranking, we run NCGD to compare the relative rank that the users assigned to our ranking.
WIth a final survey, we plan to ask people to rank the authoritativeness of the top k users in our rank with anonymized and non-anonymized tweets.

13. Evaluation

14. Conclusion
While the baseline had more authorities who seemed generic, such as news Twitter accounts, our results show more topical authorities.
We have also analyzed the sentiment influence of the top authorities, which can have further applications in formulating better marketing strategies for products and to influence consumers.
Further, we plan to include evaluation results in our final report, and also improve upon the features related to the follower-following graph.

16. References
[1] Pal, Aditya, and Scott Counts. "Identifying topical authorities in microblogs." Proceedings of the fourth ACM international conference on Web search and data mining. ACM, 2011.
[2] Weng, Jianshu, et al. "Twitterrank: finding topic-sensitive influential twitterers." Proceedings of the third ACM international conference on Web search and data mining. ACM, 2010.
[3] Cha, Meeyoung, et al. "Measuring User Influence in Twitter: The Million Follower Fallacy." ICWSM (2010): 30.
[4] Yoshida, M., & Yamaguchi, Y. (2015). Interactive Tagging Networks (Following/Followers and Tags on 1 million Twitter Users) [Data set]. Zenodo.
[5] Page, Lawrence, et al. "The PageRank citation ranking: bringing order to the web." (1999).
[6] Bishop, Christopher M. "Pattern recognition." Machine Learning 128 (2006).

17. Baseline Results NWF TIME Huffingtonpost NOLAnews Reuters CBSNews
LATenvironment
kate_sheppard
MotherNatureNet
mparent77772