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It’s not in their tweets: Modeling topical expertise of Twitter users
1. It’s not in their tweets: Modeling topical expertise
of Twitter users
Claudia Wagner, Vera Liao, Peter Pirolli, Les Nelson and Markus Strohmaier
Amsterdam, 16.4.2012
3. 3
Motivation
On Twitter information consumption is mainly
driven by social networks
Users need to decide whom to follow in order to
get trustful and relevant information about the
topics they are interested in
Evidence from real-life
Search online for evidence
4. Searching for evidence at
Twitter user’s profile page
Bio
List Memberships
Tweets and Retweets
5. 6
Research Questions
How useful are different types of user-related
data for humans to inform their expertise
judgments of Twitter users?
How useful are different types of user-related
data for learning computational expertise
models of users?
6. User Study
Expertise Judgments of humans
16 participants
Task: Rate (1-5) expertise level of selected Twitter users (with
high and low expertise) for the topic „semanticweb“
3 Conditions under which the user accounts were presented to
subjects:
Condition 1: Tweets, Retweets, List, Bio
Condition 2: Only Tweets and Retweets are shown
Condition 3: Only List and Bio are shown
For each condition and expertise level we have 4 Twitter pages
(4 replicates)
4 * 3 * 2 = 24 pages to rate per subject
7. User Study
Expertise Judgments of humans
2-way ANOVA
cond 1 (tweets, bio and lists)
cond 2 (only tweets)
3.5
Within-Subject Variables: cond 3 (only bio and lists)
•Twitter user expertise (high/low)
•3 Conditions
Mean Rating per Twitter User
Interaction between conditions and
3.0
Twitter user expertise is significant
(F(2) = 8,326 , p < 0,01 )
Post-Hoc Test shows that users’
2.5
ability to correctly judge expertise of
Twitter users differs significantly
under condition 1 and 2 and
condition 2 and 3. Low Expertise High Expertise
8. 9
Research Questions
How useful are different types of user-related
data for humans to inform their expertise
judgments of Twitter users?
How useful are different types of user-related
data for learning computational expertise
models of users?
9. 10
Dataset
10 topics
semanticweb, biking, wine, democrat, republican,
medicine, surfing, dogs, nutrition and diabetes
We use Wefollow directories as a manually
created proxy ground truth for expertise
Top 150 users per Wefollow directory
Excluded users who are in more than one of the
10 directories and users who mainly tweet non-
english
10. 11
Dataset
1145 users
Most recent 1000 tweets and retweets
Most recent 300 user lists
Bio info
Information on Twitter is sparse
Extend URLs in Tweets, RTs and bio
Use list names as search query terms
Use top 5 search query result snippets obtained
from Yahoo Boss to enrich list information
3
11. Computational Expertise Models
Methodology
Learn latent semantic structures (topics) from Twitter
communication by fitting an LDA model
T1 T2 T3
Top 20 stemmed words of 3 randomly select topics learned by an LDA model
with T=50
12. Computational Expertise Models
Methodology
Associate users with topics by using statistical Inference based
on different types of user related data user’s topical expertise
profile
Bio
T1 T2 T3
Lists
T1 T2 T3
Tweets
T1 T2 T3
RTs
T1 T2 T3
14. 15
Types of User Lists
Manual inspection of user lists
Selected 10 users at random and inspected their
user list memberships (455 user lists)
We found 3 main classes of user lists:
Personal judgments (e.g., “great people”, “geeks”)
Personal relationships (e.g., “my family”,“colleagues”)
Topical Lists (e.g., “science”, “researcher”, “healthcare”)
15. 16
Value of User Lists
3 human raters judged if a list (label and/or
description) belongs to the class Topical Lists
77,67% of user lists were topical lists
Inter-rater agreement Kappa=0.62
16. Quantify the Value of
17
Lists/Bio/Tweets/RTs
Which type of information reflects best the
topical expertise of a user?
Information Theoretic Evaluation
Which type of topic distribution reflects best the underlying
category information of the user?
Normalized Mutual Information (NMI) between user’s topic
distributions and user’s Wefollow directory
Task-based Evaluation
Which type of topic distributions are most useful for classifying
users into their Wefollow directories?
F1-score of classifcation models
17. Information-Theoretic Evaluation of
18
Computational Expertise Models
0.7
Tweet
Bio
List
0.6
Retweet
0.5
NMI
0.4
0.3
0.2
T=10 T=50 T=80 T=200 T=400 T=600
#Topics
18. Task-based Evaluation of
Computational Expertise Models
Compare topic distributions inferred via different
types of user-related data within a classification
task
Objective: Classifying users into Wefollow directories
by using topic distribution as features
Classification Task:
Train Partial Least Square classifier with topic
distributions inferred via different types of user-related
data as features
Perform 5-fold-cross validation
Use F-measure (harmonic mean of precision and
recall) to compare classifiers’ performance
19. F−Measure
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
biking
Bio
List
Tweet
Retweet
democrat
diabetes
dogs
medicine
nutrition
republican
semanticweb
surfing
wine
Computational Expertise Models
Task-based Evaluation of
21. Task-based Evaluation of
Computational Expertise Models
T=300 List Tweet
wine
wine
surfing
surfing
semanticweb semanticweb
republican republican
nutrition nutrition
medicine medicine
dogs dogs
diabetes diabetes
democrat democrat
biking biking
diabetes
dogs
biking
medicine
nutrition
republican
semanticweb
surfing
wine
democrat
diabetes
dogs
biking
medicine
nutrition
republican
semanticweb
surfing
wine
democrat
x-axis shows reference values
y-axis shows predictions
22. Conclusions
Different types of user-related data lead to
different topic annotations
List-based topic annotations are most distinct from all others
Bio-, tweet- and retweet-based topic annotations are quite similar
For creating topical expertise profiles of users
information about their list memberships is most
useful
For informing humans’ expertise judgments about
Twitter users contextual information (user’ bio and
list memberships) is most useful
23. 24
Implications & Limitations
User Interface
Make user lists and bio information more prominent
Incentives for people to use lists more heavily
E.g. provide weakly list-summaries
Search and Recommender Systems could benefit
from exploiting user list information
Results are biased towards users with high
Wefollow rank
24. Bio and User Lists are useful for judging topical expertise
Experimental Setup
THANK YOU
claudia.wagner@joanneum.at
http://claudiawagner.info
src: http://adobeairstream.com/green/a-natural-predicament-sustainability-in-the-21st-century/
Hinweis der Redaktion
Letmestartbymotivating a bittheresearchwearedoing. On socialmediaapplicationsliketwitterwhereeveryonecanprovideinformation.Thereforejudgingtopicalexpertiseofusersisoftencrucial in ordertodecideiftheinformationprovidedbythisuseristrustfuland relevant.
So whatpeopleusually do thanisto check out theprofilepageoftheTwitterusertheyareinterested in in orderfigure out whothispersonis an what he/sheisdoing. On theprofilepagepeopleareconfrontedwith 4 different typesoftextualinformationwhichmaypotentiallyrvealinformationabouttheuser‘sexpertise: on the top ofthepagethey find thebioinformation, belowethebiosectionthey find themostrecenttweetsandrts. On therightsideofthepagethey find themostrecentlistmembershipsof a user.
In thisworkwetrytoadressthefollowing 2 researchquestions
To address our first RQwe conducted a user study to find out which type of user related data are most useful for humans to make sense about the expertise of a Twitter user.
One can see from this that content is not enough to inform users’ expertise judgments. Our participants were not able to differentiate experts from novices just by seeing their tweets.Contextual information – i.e. lists and bio – were more useful for informating expertise judgements and the best performance was achieved when participants saw everything – i.e. tweets, lists and bio information.
Forthesecondexperimentwecreated a datasetasfollows.Users with high Wefollow rank are nominated by many other users in that directoryPrevious research (which won the best paper at this conf in 2010) showed that users with high Wefollow rank tend to be perceived as topical experts.
Weendeduphaving a bitmoprethan 1100user
If one wants to create topical expertise profiles of users, the first one needs is to learn topics. We used our dataset to learn semantically cohesive bag of words (i.e. topics) - i.e. we fitted a topic model (LDA) to our dataset where the aggregation of all data per user was a document.
Next we used statistical inference to related users with topics. We used 4 types of user-related data to infer the topic distribution of a user.
Our results show that on average topic profiles of users inferred from… This indicates that listscontain other types of information. One potential explanation is that users add other users to list which reflect what they know about them from real life. So e.g. I might add a colleague of mine into a list called “semantic web researcher” although this person does not use Twitter at all for talking about semantic web.
Togainfurtherinsightintothenatureoflistswedecidedtoinspect a small sample oflistsmanually.
The thirdcategoryoflistsismostusefulforourtask. Thereforewewantedtoknowhowmanypercentoflistsbelongtothisclass.
So farweonlyknowthatinformationobtainedfromuserlistsdifferssignificantlyfromothertypesofinformationandthatmanyuserlistsaretopicallists, but wedontknowwhich type ofinformationbestreflectsthetopicalexpertiseofusers.
The NMI measures the mutual dependence of the two random variables. A higher NMI value implies that a topic distribution more likely matches the underlying category information. List-based topic distributions reflect the underlying cat. Info best – i.e. users in one Wefollowdir tend to be in topical similar lists.
So far we only know that different types of user-related data lead to different topic distributions but we don’t know which topic distributions are best – i.e. reflect the expertise of a user most accurate. To answer this question we compared different topic distributions within a classification task. The aim of the classification task was to classify users into the right wefollow directory. We only used Wefollow users with a high rank (and previous research has that users with high wefollow rank tend to be perceived as experts for that topic) who only showed up in one of the 10 directories. Therefore, we assume that the topic distribution which allows classifying users most accurately into their expertise area this the one who reflects users’ expertise most accurately.
Our results show that for all 10 Wefollow directories a classifier trained with list-based topic distributions as features performs best – i.e. leads to the highest F-score.
Also the number of topics we learn may impact the performance of our classifier. We therefore compared the F-score of classifiers trained with different number of topics. From this graph we can see again that list-based topic annotations lead to the best performing classifier not matter how broad or fine grained topics are.
Also by inspecting the confusion matrices one can see that a classifier trained with list-based features shows less confusion. An optimal classifier would lead to a red box with a white diagonal line. The x-axis of each confusion matrix shows the reference values and the y-axis shows the predictions. The lighter the color the higher the value.