1. Information Propagation Speed and Patterns in Social Networks: a Case Study Analysis of German Tweets
International Conference of Algorithms, Computing
and Systems, 10th-Aug, South Korea
Raad Bin Tareaf – Social Media Analysis
Internet Technologies and Systems
Hasso Plattner Institut- Digital Engineering Faculty

2. Motivation
“Local Trends will allow you to learn more about the nuances in our world and discover even more relevant topics that might matter to you.”
- By Twitter Inc./blogsite.
Information propagation
Information Propagation Speed
Raad Bin Tareaf,
Internet Technologies and Systems

3. What is Information propagation Speed ?
Twitter launched in Mar-2006, structured data.
Social Network are graph-based.
Number of interactions (re-tweets) between users
within timeframe called scale, infection builds cascade (range).
Information Propagation Speed
Raad Bin Tareaf,
Internet Technologies and Systems

4. Related Work
Kwak et al:`influncers can be identified by calculating the page rank for a set of Twitter users´.
Hennig et al: `used information retrieval approches to identify trend inside unstructured blog data`.
Yang et al: ´divided the diffusion into three major properties: Speed,scale,range´
Lack of information concerning difference between local and wordwide diffused tweets.

5. Data Collection- Cronjob
Scheduler (15 minutes):
Top 10 trends,
4 places per day
APIs:
Live Streaming search
Collect tweets
JSON processing:
metadata, status, tweetID
Data Cleansing
Pushes data into database( local: MySQL, remote: HANA)
Save to
.json

6. Concept – Trends
For each trend, settle all tweets which were posted during the same hour inside one chunk. Why?
Analyzing histograms lead to discover propagation patterns:
- Number of tweets decreases during the local night time
- Others, did not decreases and kept active
Information Propagation Speed
Raad Bin Tareaf,
Internet Technologies and Systems

7. Indicators
The time points from (i – 1, i , i+ 1) are investigated, count retweets number over a day and check if there is a significant increase (51%) in the re-tweet amount for each hour bucket.
Now, there is enough change in the tweet/hastag
status to trigger process of categorization using four
main indicators:
Information Propagation Speed
Raad Bin Tareaf,
Internet Technologies and Systems

8. Automated Analysis Indicators
Day-Night Circle Indicator
Night-Inactivity Indicator
Language Indicator
Short-Day trend Indicator
Information Propagation Speed
Raad Bin Tareaf,
Internet Technologies and Systems

9. Day-Night Circle Indicator
Trends which are only
valid for a certain reigon
/country follows a characterstics
of day-night cycle.
Information Propagation Speed
Raad Bin Tareaf,
Internet Technologies and Systems

10. 2. Night-Inactivity Indicator
All trends by Day-Night cycle are as well considerd local by Night-Inactivity indicator.
Opposite? Then clear indication that trend is becoming global.
Information Propagation Speed
Raad Bin Tareaf,
Internet Technologies and Systems

11. 2. Night-Inactivity Indicator
Information Propagation Speed
Raad Bin Tareaf,
Internet Technologies and Systems

12. 3. Language Indicator Map of Tweet and Language
most of the tweets (>80%)" are in one language?
Does not apply for English
#DavidBowie: 84% English
#Diekmann: 95% German
Information Propagation Speed
Raad Bin Tareaf,
Internet Technologies and Systems

13. 4. Short Day Trend Indicator
Dose not even satisfy the whole day-night cycle.
→ Genuine indicator for locality, since global trends stay much longer active.
Information Propagation Speed
Raad Bin Tareaf,
Internet Technologies and Systems

14. 4. Short Day Trend Indicator
Presentation Title
Speaker, Job Description, Date if needed

15. Dataset Statistics (1) Number of full-structured Tweets: 1.2 m
Number of trendy hashtags: 291
Twitter uses WOEIDs to identify place
around the world
Information Propagation Speed
Raad Bin Tareaf,
Internet Technologies and Systems

16. Dataset Statistics (2) Information Propagation Speed Raad Bin Tareaf,
Internet Technologies and Systems

17. Languages Distribution for Hashtags
Information Propagation Speed
Raad Bin Tareaf,
Internet Technologies and Systems

18. Dataset Insights Dataset contains most followed users:
( )
( )
( )
( )
Most active user:
tweets,
(second most: ) → bots

19. Detecting Local to Global Transformation
The Algorithm check iteratively every hour if there are a significant change in re-tweets amount for the past 3 days.
Factors taken into consideration :
Tweets Scale
Time Irregularities
Language Distribution
Information Propagation Speed
Raad Bin Tareaf,
Internet Technologies and Systems

20. Local vs. Global Trends (time,Language,Locaiton)
Information Propagation Speed
Raad Bin Tareaf,
Internet Technologies and Systems

21. Local vs. Global Trends (9th – 11th JAN)
Presentation Title
Speaker, Job Description, Date if needed

22. Observations External Influences Bots with enormous amout of tweets
Considering every re-tweet as a
relation, calculating HITS scores produce
high hub and authority scores for users
who get retweeted often.
Limitation of Twitter API
Information Propagation Speed
Raad Bin Tareaf,
Internet Technologies and Systems

23. Future Work Analyse the influence of social media bots.
Build ranking predictions for influncers.
Verify local-global categorization on further datasets.
Identify new propagation patterns.
Information Propagation Speed
Raad Bin Tareaf,
Internet Technologies and Systems

24. Conclusion Implementation of automated analysis indicators:
1. Day-Night Circle Indicator 3. Language Indicator
2. Night-Inactivity Indicator 4. Short-Day trend Indicator
Local and global trends allow to discover how big the influence and how fast the propagation speed of a trend is and in which way a similar trend will evolve.
Friends connections can, once they are identified, be used as initial marketing campaigns.

25. Discussion
[1] J. Yang and S. Counts. Predicting the Speed, Scale, and Range of Information Diffusion in Twitter, ICWSM,10: , 2010.
[2] A. Java, X. Song, T. Finin and B. Tseng. Why we twitter: understanding microblogging usage and communities. In Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, pages ACM, 2007.
[3] H. Kwak, C. Lee, H. Park and S. Moon. What is Twitter, a social network or a news media? In Proceedings of the 19th international conference on World wide web, Pages ACM 2010.
[4] M. Cha, H. Haddadi, F. Benevenuto and P.K. Gummadi. Measuring user influence in twitter: The million follower fallacy. ICWSM 10(10- 17):30, 2010.
[5] P. Hennig, P. Berger, C. Lehmann, A. Mascher and C. Meinel. Accelerate the detection of trends by using sentiment analysis within the blogosphere, In Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on, pages IEEE, 2014.
[6] M. Naaman, H. Becker and L. Gravano. Hip and trendy: Characterizing emerging trends on Twitter. Journal of the Association for Information Science and Technology, 62(5): , 2011.
Dataset is available:
Implementation:
Information Propagation Speed
Raad Bin Tareaf,
Internet Technologies and Systems