Extracting Interest Tags fromTwitter User Biographies

Ying Ding, Jing Jiang

School of Information Systems

Singapore Management University

AIRS 2014, Kuching, Malaysia

Social Media and Personal Data

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• Much personal information revealed in social media– Content, links, ratings

personal preferences

• All this information is useful to– Researchers: social science– Businesses: targeted

advertising

User Biographies in Twitter

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• Self-introductions written in free form• Reflect users’ background and interests

User Biographies in Twitter

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profession interestsage

Around 28% of Singapore Twitter users and 50% of US Twitter usersrevealed their personal interests in their biographies.

Dong Wei et. al. Who am I on Twitter?: A cross-country comparison.WWW’2014

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Outline

• Background

• Our task

• Syntactic patterns of interest tags

• Build training data + gold standard

• Method

• Experiments

• Summary

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Our task

• Automatically extract phrases that describe a user’s personal interests.– We call them “interest tags”– A typical information extraction problem.– Automatically build training data based on

common syntactic patterns.

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Method

• Linear Chain CRF

• BIO labels

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Syntactic Patterns of Interest Tags

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• Based on manual annotation of 500 user biographies.• 28.8% of user biographies contain meaningful interest tags.

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Building Training Data

• Seed patterns:

– Play + [NP]

– [NP] + fan

– Interested in + [NP]

• Steps:

– Use seed patterns to extract noun phrases and rank them according to their frequency

– Pick the top-100 ranked noun phrases and use them as positive instances to train CRF

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Features• Syntactic or dependency features are not used as the

Twitter text is noisy for parsing• Both lexical and POS tag feature are used• To avoid over-fitting: only features extracted from the

surrounding tokens for each position are used

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Gold Standard

• Two annotators: graduate students

• 500 randomly sampled user biographies

• 1190 sentences– Two annotators disagree on 10 sentences– High agreement

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Experiment

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BL-700: top 700 frequent phrases, we choose 700 because it gets the highest F-score among various numbers.Seed: use seed patterns to recognize interest tags

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Extracted Patterns

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Some popular patterns are:•[Interest tag] + fan/lover/enthusiast•I love + [interest tag]•[interest tag] is/are my life

Is it difficult to predict interest tags by users’ tweets?

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Is it difficult to predict interest tags by users’ tweets?

We also applied Tf-idf ranking, which has been used to extract

personalized user tags, to extract user interest tags.

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• Interest tags extracted from user’s biographies are not necessarily reflected in a user’s post tweets.• They can work as supplementary information when profiling a user.

Summary

• We studied the problem of extracting interest tags from Twitter user biographies

• We automatically built noisy training data based on syntactic patterns

• We trained CRF classifier on the noisy training data and achieved decent performance

• Interest tags extracted from Twitter user biographies may not be reflected in user’s tweets

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Thank you!

Questions?

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