1. Beyond the Words: Predicting User Personality from Heterogeneous Information
Presenter: Benyi Gong

2. motivation
User personality is not only essential to many scientific disciplines, but also has a profound business impact on practical applications such as digital marketing, personalized recommendation, mental diagnosis, and human resources management.
Language usage in social media is effective in personality prediction. However, leveraging the heterogeneous information on social media could have a better understanding of user personality!

3. Paper structure PROBLEM DEFINITION HIE Structure
HETEROGENEOUS FEATURE ENGINEERING
Experiments and Results
Conclusion and Discuss

4. PROBLEM DEFINITION
4 types of digital trace data: tweet, emoticon, avatar, and responsive pattern
The five factor model in personality: Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness.
Personality Prediction Evaluation

5. HIE structure

6. strategies to extract semantic representations
Tweets: LIWC, Pearson correlation, bag-of-words clustering, and Text-CNN
Avatars: deep learning, k-means clustering
Emoticons: Pearson Correlation, Emotion Mapping
Responsive Pattern: Responsive-CNN

7. Experiments and results

8. Conclusion and discuss
Invent HIE to predict user personality by integrating heterogeneous information in digital traces including self-language usage, avatars, emoticons, and responsive patterns.
Extensive experiments and analysis on a real-world dataset covering both personality survey results and social media usage from 3,162 volunteers. The results are promising and HIE outperforms the state-of-the-art models in all Big Five personality dimensions.
Limitations and how to use it in test retrieval model?

9. Questions?