Topical search in Twitter

Complex Network Research GroupDepartment of CSE, IIT Kharagpur

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

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

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, …

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

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

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

How Lists work ?

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

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, …

Inferring topical attributes of users

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)

Mining Lists to infer expertise

Unigrams and bigrams considered as topics

Result: Topics for U along with their frequencies in the document

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

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

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

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-sws.org/who-is-who/Inferring Who-is-who in the Twitter

Social Network, WOSN 2012 (Highest rated paper in workshop)

Identifying topical experts

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

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

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 TU of user and Q

Popularity of user – number of Lists including the user

Cover Density ranking preferred for short queriesTopic relevance( TU, Q ) × log( #Lists including U )

Cognos Search system for topical experts in Twitter

Publicly deployed athttp://twitter-app.mpi-sws.org/whom-to-follow/

Cognos: Crowdsourcing Search for Topic Experts in Microblogs, ACM SIGIR 2012

Cognos results for “politics”

Cognos results for “stem cell”

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

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

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

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

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

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

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

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

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

Topical search in Twitter

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

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

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

Advantages of list-based methodology

600K experts on 36K distinct topics

TopicalDiversityofExpertSample

CSCW’14

PopularTopics

NicheTopics

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.

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

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

What-is-happening on Twitter

twitter-app.mpi-sws.org/what-is-happening/

Topical search in Microblogs with Cognoscenti, Or: The Wisdom of Crowdsourced Experts,

Results for thelast week on

Politics (a popular topic)

Related tweets aregrouped together bycommon hashtags.

Number of expertstweeting on the subjectand the number of tweetson the subject decidesranking.

The most popular tweetfrom the mostauthoritative userrepresents the group.

Our system specially excels for niche topics.

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

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

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

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

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.

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.

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)

Complex Network Research Group

Thank You

Contact: niloy@cse.iitkgp.ernet.in

Complex Network Research Group (CNeRG) CSE, IIT Kharagpur, Indiahttp://cse.iitkgp.ac.in/resgrp/cnerg/