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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

Dataset Insights Dataset contains most followed users: ~@KatyPerry (95.431.353) ~@JustinBieber (90.886.180) ~@TaylorSwift13 (83.115.401) ~@BarackObama (80.652.888) Most active user: 6.843.102 tweets, ~@notiven (second most: 6.628.870) → bots

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

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

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

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

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

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.

Discussion [1] J. Yang and S. Counts. Predicting the Speed, Scale, and Range of Information Diffusion in Twitter, ICWSM,10: 355-358, 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 56-65. 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 591-600. 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 503- 508. 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):902-918, 2011. Dataset is available: https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:73236 Implementation: https://github.com/raadbintareaf/IPS_Meassurement Information Propagation Speed Raad Bin Tareaf, Internet Technologies and Systems