{ Trends in Social Network M. Tech Project Presentation By : Pranay Agarwal 2008CS50220 Guides : Amitabha Bagchi Maya Ramanath

 Introduction  Why twitter ?  Filter model for tweets  Social Graph construction  Evolving graphs for topics  Results and Conclusion  Future work Outline

Introduction : Trends 2.4 B Online Facebook 1.2 B Twitter 200 M [1] Age old query : “what are people talking about” ?

 Search Engines  News Media  Online Contents  E-Commerce

 200 Millions users, 400 Millions tweets everyday. [2]  All major news sources, Govt. offices etc.  Fast and real time  Data exposed via Twitter API. Why Twitter

Needle in Haystack

Tweet basedMeta dataNLP tags Word countRetweet countNERs Mentions countFollowers countPOS Hash tags countURLs count -Source (mobile, web) S(t) = Σ ( Wi ∗ Fi(t) ) S (t) = score of tweet t Wi = Weight of feature I Fi(t) = feature i value for tweet t Model Features Filter Model for tweets

  Too short tweets (<= 2 words) are always “chat” tweets.   40 % of the “chat” tweets had one of the stop words while only 2% of the “informative” tweets had it. Stop words = (I, me, mine, you, yours etc.) Two Phase model

Goal : “Enhance Timesense (Yahoo! proprietary) capabilities using twitter.” “Enhance Timesense (Yahoo! proprietary) capabilities using twitter.” Tasks :  Marcelo Filter model  Trend prediction using Social Graph. Yahoo! Proposal

 Collecting data from Twitter API.  Storing and processing Social Graph.  Topic wise clusters of tweets. Spatio Temporal Analysis of Topic Popularity in Twitter Evolving graph for a topic The hypothesis was that during evolution of graph, its structure and topology gives rise to patterns, which could act as distinct features to distinguish “trending” topic from “non-trending” topic.

  Wrote a python library to communicate with the API.   Collecting friends and followers relations   Several instances of nodes making calls to Twitter API under normal rate limit.[3]   Frequent outages in the API service causing further delay and blocking.   Resolve first for “Good” users, who will be involved in creating and sharing of “informative” tweets. Twitter API

  Node : Each node is a user, which also contains several other details of user profiles. We also label users for which we have resolved all the relations.   Edge : An directed edge edge(u → v) represents user “u” follows user “v” stored in Adjacency List.   Index : We need to make several queries where given a user details we want to get it’s all followers and friends. To make this query fast and efficient we indexed the graph by a unique key “uid” as user id of all user nodes. This “uid” is same as the twitter user id, which is already present in the tweet object. Graph Database Neo4j [4]

  30 Millions nodes and 60 Millions edges.   Our graph is only 10% of the whole twitter graph. Validation   Almost 95% of the top celebs present   Around 60% of the users of the second set present in our graph This is a very strong indication that our graph mostly contains “active” and “good” users while there could be significant fraction of twitter users as “inactive”

  “Timesense”, a Yahoo! Proprietary service, which gives list of topic search queries along with “buzz” score which indicates the “trendiness”   The search queries returned by Timesense are not clustered together, which means different search queries related to same event is given as different queries Tweets Clustering mark appel houston mlb draft mark appel and pat appel mark appel 2013 mlb draft mark appel contract what high school did mark appel go to mark appel major stanford mark appel stanford baseball baseball player mark appel of stanford

  Users in twitter use different variants of the same topic   We implemented a Bi-gram matching algorithm to cluster together search queries like these.   One pass of all the public tweets and fetch the tweet if it contains any of the bi-gram terms pair in it. N gram Matching

“Topics that are going to become very popular witness intense discussion within communities at first. When the level of intensity rises then the users who bridge communities enter the discussion in a big way causing a merging of what were earlier disjoint discussions.” Evolving Graphs

  The vertex set of Gt 0 comprises the users V 0 who tweet about t on window 0 (the edge set is empty)   The vertex set V i t of Gt i is the set of all users who have tweeted on a topic in windows 0 through i   An edge(u ← v) is added to E i t if u ∈ V (Current set) and v has tweeted about t on window i Window = 30 Minutes. Algorithm

Topic no.Trend TypeTopic 1HighIRS Scandal of Obama administration 2HighAngelina Jolie going through mastectomy 3LowCannes Film Festival LowThe Great Gatsby (Movie 2013) Experiments

High Trend : First row, Low Trend : Second Row

High Trends

Low Trends

  Largest component size increases for all the topics. But the increase in the size is much more significant in case of topic 1 and 2   Topics 1 and 2 contain most of its nodes in the largest   low ratio c1/c2 for topics 2 and 3 shows that there are many small independent clusters of communities discussing among themselves without leading to a large component component. Topic C1C1/C

  Users who bridge communities enter the discussion   Bridge users serve as a barometer of the topics rising popularity External edges = {(u → v) : u ∈ S, v ∈ V \ S} Total Edges = |{(u → v) : u ∈ S}| φ(S) = External Edges / Total edges Conductance

Resolve Missing Edges Topic No.NatureTopic 1Very LowMattapoisett Car accident 2MildJeep Patriot new model car 3TrendMayor Bloomberg 4High trendMark Appel Collecting tweets clusters for these topics using above Algorithm Resolving all relations for all the authors in these tweets.

First row : low, Second row: high Trend

Low Trends

High Trends

  Table shows the higher conversion of external to internal edges, in case of trending topics, which means more behavior influence and spreading to followers in case of trends.   Largest connected component contains around 15 % and 35 % of all users in case of topic 1 and 2 respectively, while in case of topic 3 it is 80 % and > 90% in case of topic 4.This strongly supports the hypothesis that in case of trending topics, users form large connected community. TopicE/NL/N %Fall in conductance

 Resolve all relations ?  May be NOT.. Limitations 0.6 Drop 0.1 Drop

 Identify good “sensors” or users  Resolve as many relations possible  Better topic detection and clustering from tweets.  Efficient Graph processing data Structure  Inter relation of topics Future Work

Thanks

  [1] m   [2] Twitter official blog   [3] Twitter api   [4] neo4j   S. Ardon, A. Bagchi, A. Mahanti, A. Ruhela, A. Seth, R. M. Tripathy, and S. Triukose. Spatio- temporal analysis of topic popularity in twitter. CoRR, abs/ , References