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Topical search in Twitter Complex Network Research Group Department of CSE, IIT Kharagpur.

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Presentation on theme: "Topical search in Twitter Complex Network Research Group Department of CSE, IIT Kharagpur."— Presentation transcript:

1 Topical search in Twitter Complex Network Research Group Department of CSE, IIT Kharagpur

2 Topical search on Twitter Twitter has emerged as an important source of information & real-time news  Most common search in Twitter: search for trending topics and breaking news Topical search  Identifying topical attributes / expertise of users  Searching for topical experts  Searching for information on specific topics

3 Prior approaches to find topic experts  Research studies  Pal et. al. (WSDM 2011) uses 15 features from tweets, network, to identify topical experts  Weng et. al. (WSDM 2010) uses ML approach  Application systems  Twitter Who To Follow (WTF), Wefollow, …  Methodology not fully public, but reported to utilize several features

4 Prior approaches use features extracted from  User profiles  Screen-name, bio, …  Tweets posted by a user  Hashtags, others retweeting a given user, …  Social graph of a user  #followers, PageRank, …

5 Problems with prior approaches  User profiles – screen-name, bio, …  Bio often does not give meaningful information  Information in users profiles mostly unvetted  Tweets posted by a user  Tweets mostly contain day-to-day conversation  Social graph of a user – #followers, PageRank  Does not provide topical information

6 We propose … Use a different way to infer topics of expertise for an individual Twitter user Utilize social annotations  How does the Twitter crowd describe a user?  Social annotations obtained through Twitter Lists  Approach essentially relies on crowdsourcing

7 Twitter Lists A feature used to organize the people one is following on Twitter  Create a named list, add an optional List description  Add related users to the List  Tweets posted by these users will be grouped together as a separate stream

8 How Lists work ?

9 Using Lists to infer topics for users If U is an expert / authority in a certain topic  U likely to be included in several Lists  List names / descriptions provide valuable semantic cues to the topics of expertise of U

10 Dataset Collected Lists of 55 million Twitter users who joined before or in 2009  88 million Lists collected in total All studies consider 1.3 million users who are included in 10 or more Lists Most List names / descriptions in English, but significant fraction also in French, Portuguese, …

11 Inferring topical attributes of users

12 Mining Lists to infer expertise Collect Lists containing a given user U List names / descriptions collected into a ‘document’ for the given user Identify U’s topics from the document  Handle CamelCase words, case-folding  Ignore domain-specific stopwords  Identify nouns and adjective  Unify similar words based on edit-distance, e.g., journalists and jornalistas, politicians and politicos (not unified by stemming)

13 Mining Lists to infer expertise Unigrams and bigrams considered as topics Result: Topics for U along with their frequencies in the document

14 Topics inferred from Lists linux, tech, open, software, libre, gnu, computer, developer, ubuntu, unix politics, senator, congress, government, republicans, Iowa, gop, conservative politics, senate, government, congress, democrats, Missouri, progressive, women celebs, actors, famous, movies, comedy, funny, music, hollywood, pop culture

15 Lists vs. other features love, daily, people, time, GUI, movie, video, life, happy, game, cool Most common words from tweets celeb, actor, famous, movie, stars, comedy, music, Hollywood, pop culture Most common words from Lists Profile bio

16 Lists vs. other features Fallon, happy, love, fun, video, song, game, hope, #fjoln, #fallonmono Most common words from tweets celeb, funny, humor, music, movies, laugh, comics, television, entertainers Most common words from Lists Profile bio

17 Who-is-who service Developed a Who-is-Who service for Twitter Shows word-cloud for major topics for a user http://twitter-app.mpi- http://twitter-app.mpi- Inferring Who-is-who in the Twitter Social Network, WOSN 2012 (Highest rated paper in workshop)

18 Identifying topical experts

19 Topical experts in Twitter 400 million tweets posted daily Quality of tweets posted by different users vary widely  News, pointless babble, conversational tweets, spam, … Challenge: to find topical experts  Sources of authoritative information on specific topics

20 Basic methodology Given a query (topic) Identify experts on the topic using Lists  Discussed earlier Rank identified experts w.r.t. given topic  Need ranking algorithm Additional challenge: keeping the system up-to-date in face of thousands of users joining Twitter daily

21 Ranking experts Used a ranking scheme solely based on Lists Two components of ranking user U w.r.t. query Q  Relevance of user to query – cover density ranking between topic document T U of user and Q  Popularity of user – number of Lists including the user Cover Density ranking preferred for short queries Topic relevance( T U, Q ) × log( #Lists including U )

22 Cognos Search system for topical experts in Twitter Publicly deployed at Cognos: Crowdsourcing Search for Topic Experts in Microblogs, ACM SIGIR 2012

23 Cognos results for “politics”

24 Cognos results for “stem cell”

25 Evaluation of Cognos - 1 Competes favorably with prior research attempts to identify topical experts (Pal et al. [WSDM 2011])

26 Evaluation of Cognos – 2  Cognos compared with Twitter WTF  Evaluator shown top 10 results by both systems  Result-sets anonymized  Evaluator judges which is better / both good / both bad  Queries chosen by evaluators themselves  27 distinct queries were asked at least twice  In total, asked 93 times  Judgment by majority voting


28 Cognos vs Twitter WTF  Cognos judged better on 12 queries  Computer science, Linux, mac, Apple, ipad, India, internet, windows phone, photography, political journalist  Twitter WTF judged better on 11 queries  Music, Sachin Tendulkar, Anjelina Jolie, Harry Potter, metallica, cloud computing, IIT Kharagpur  Mostly names of individuals or organizations  Tie on 4 queries  Microsoft, Dell, Kolkata, Sanskrit as an official language

29 Cognos vs Twitter WTF  Low overlap between top 10 results  … In spite of same topic being inferred for 83% experts  Major differences are due to List-based ranking  Top Twitter WTF results – mostly business accounts  Top Cognos results – mostly personal accounts


31 Keeping system up-to-date Any search / recommendation system on OSN platform needs to be kept up-to-date  Thousands of new users join every day  Need efficient way of discovering topical experts Can brute force approach be used?  Periodically crawl data (profile, Lists) of all users

32 Scalability problem  200 million new users joined Twitter during 9 months in 2011  740K new users join daily  Lower-bound estimate: 1480K API calls per day required to crawl their profiles and Lists  Twitter allows only 3.6K API calls per day per IP  480K API calls per day from whitelisted IP  Plus, 465 million users already

33 How many experts in Twitter?  Only 1% listed 10 or more times  Only 0.12% listed 100 or more times  If experts can be identified efficiently, possible to crawl their Lists

34 Identifying experts efficiently  Hubs – users who follow many experts and add them to Lists  Identified top hubs in social network using HITS  Crawled Lists created by top 1 million hubs  Top 1M hubs listed 4.1M users  2.06M users included in 10 or more Lists (50%)  Discovered 65% of the estimated number of experts listed 100 or more times

35 Identifying experts efficiently  More than 42% of the users listed by top hubs have joined Twitter after 2009  Discovered several popular experts who joined within the duration of the crawl  All experts reported by Pal et. al. discovered  Discovered all Twitter WTF top 20 results for 50% of the queries, 15 or more for 80% of the queries

36 Topical search in Twitter

37 Looking for Tweets by Topic Services today are limited to keyword search  Knowing which keywords to search for, is itself an issue  Keyword search is not context aware Tweets are too small to deduce topics Topic analysis of 400M tweets/day is a challenge

38 Challenges Some tweets are more important than others  Millions of tweets are posted on popular topics  Only some are relevant to the context intended Tweets may contain wrong or misleading info  Twitter has a large population of spammers  Twitter is also a potent source of rumors  Some tweets are outright malicious

39 Our Approach to the Issues Scalability  We only look at tweets from as small subset of users who are experts on different topics Topic deduction  We map user expertise topics, to tweets/hashtags, instead of the other way round Trustworthiness  Our source of tweets is a small subset of users  It is practical to vet their expertise and reputation

40 Advantages of list-based methodology 600K experts on 36K distinct topics

41 Topical Diversity of Expert Sample CSCW’14

42 Popular Topics

43 Niche Topics

44 Challenges in Used Approach We assign topics to tweets/hashtags Inferring tweet topics from tweeter expertise  Experts can have multiple topics of expertise  Experts do tweet about topics beyond their expertise Solution: If multiple experts on a subject tweet about something, it is most likely related to the topic.

45 Sampling Tweets from Experts We capture all tweets from 585K topical experts  This is a set we obtained from our previous study  This about 0.1% of the whole Twitter population The experts generate 1.46 million tweets/per day  This is 0.268% of all tweets on twitter Expertise in diverse topics (36K)  Our topics of expertise is crowd sourced  We will have more topics as more users show interests

46 Methodology at a Glance Given a topic, we gather tweets from experts We use hashtags to represent subjects Clustering Tweets by similar hashtags  A cluster represents information on related subjects Ranking clusters by popularity  Number of unique experts tweeting on the subject  Number of unique tweets on the subject Ranking tweets by authority  Tweets from highest ranked user is shown first

47 What-is-happening on Twitter Topical search in Microblogs with Cognoscenti, Or: The Wisdom of Crowdsourced Experts,

48 Results for the last week on Politics (a popular topic)

49 Related tweets are grouped together by common hashtags. Number of experts tweeting on the subject and the number of tweets on the subject decides ranking. The most popular tweet from the most authoritative user represents the group.

50 Our system specially excels for niche topics.

51 Evaluation – Relevance We used Amazon Mechanical Turk for user evaluation  We chose to evaluate 20 topics  We picked top 10 tweets and hashtags  We picked results for all 3 time groups Users have to judge if the tweet/hashtag was relevant to the given topic  Options are Relevant/Not Relevant/Can’t Say We chose master workers only Every tweet/hashtag was evaluated by at least 4 users

52 Evaluating Tweet Relevance We obtained 3150 judgments 76% of which were Relevant 22% Not Relevant, 2% Can’t Say 80% of the Tweets were marked relevant by majority judgment

53 Dissecting Negative Judgments Iphone was the topic which received most negative results Experts on Iphone were generally tweeting on the overall topic (such as androids, tablets, …) Last week time group had most positive results  Scarcity of information led to bad ranking

54 Evaluating Hashtag Relevance Total 3200 judgments 62.3% were Relevant  Much less than tweets (76% were marked relevant) Relevance of hashtags is very context sensitive

55 Perspectival relevance The generic hashtag #sandy is very relevant to the topics in context of the tweet. These got negative judgments when shown without the tweets.

56 Generic Hashtags Some hashtags are generic, but our service brings our their specificity with respect to the topic. These hashtags received negative judgments when shown without the context of the tweet.

57 Summary Simple Core Observation Users curate experts Services  who-is who (WOSN’12, CCR’12)  whom-to-follow (SIGIR’12)  what-is-happening (in-submission)  Sample-stream (CIKM’13, CSCW’14)

58 Complex Network Research Group

59 Thank You Contact: Complex Network Research Group (CNeRG) CSE, IIT Kharagpur, India

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