short-paper

Distilling Knowledge on Text Graph for Social Media Attribute Inference

Authors:

Dinghao WuAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2024 - 2028

https://doi.org/10.1145/3477495.3531968

Published: 07 July 2022 Publication History

Abstract

The popularization of social media generates a large amount of user-oriented data, where text data especially attracts researchers and speculators to infer user attributes (e.g., age, gender) for fulfilling their intents. Generally, this line of work casts attribute inference as a text classification problem, and starts to leverage graph neural networks for higher-level text representations. However, these text graphs are constructed on words, suffering from high memory consumption and ineffectiveness on few labeled texts. To address this challenge, we design a text-graph-based few-shot learning model for social media attribute inferences. Our model builds a text graph with texts as nodes and edges learned from current text representations via manifold learning and message passing. To further use unlabeled texts to improve few-shot performance, a knowledge distillation is devised to optimize the problem. This offers a trade-off between expressiveness and complexity. Experiments on social media datasets demonstrate the state-of-the-art performance of our model on attribute inferences with considerably fewer labeled texts.

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This is the presentation video for the paper ''Distilling Knowledge on Text Graph for Social Media Attribute Inference.'' The video indicates our motivation, describes our model in detail, and briefly analyzes our results.

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Cited By

Sun ZChen ZZhang JHao D(2024)Fairness Testing of Machine Translation SystemsACM Transactions on Software Engineering and Methodology10.1145/366460833:6(1-27)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3664608
Chen ZMao HLi HJin WWen HWei XWang SYin DFan WLiu HTang J(2024)Exploring the Potential of Large Language Models (LLMs)in Learning on GraphsACM SIGKDD Explorations Newsletter10.1145/3655103.365511025:2(42-61)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3655103.3655110
Jing SChen LLi QWu D(2024)DOS-GNN: Dual-Feature Aggregations with Over-Sampling for Class-Imbalanced Fraud Detection On Graphs2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650494(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650494
Show More Cited By

Index Terms

Distilling Knowledge on Text Graph for Social Media Attribute Inference
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information retrieval
    1. Document representation

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cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
,
Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

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Published: 07 July 2022

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SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Cited By

Sun ZChen ZZhang JHao D(2024)Fairness Testing of Machine Translation SystemsACM Transactions on Software Engineering and Methodology10.1145/366460833:6(1-27)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3664608
Chen ZMao HLi HJin WWen HWei XWang SYin DFan WLiu HTang J(2024)Exploring the Potential of Large Language Models (LLMs)in Learning on GraphsACM SIGKDD Explorations Newsletter10.1145/3655103.365511025:2(42-61)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3655103.3655110
Jing SChen LLi QWu D(2024)DOS-GNN: Dual-Feature Aggregations with Over-Sampling for Class-Imbalanced Fraud Detection On Graphs2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650494(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650494
Ashmore BChen L(2024)Leveraging Homophily-Augmented Energy Propagation for Bot Detection on GraphsDatabase Systems for Advanced Applications10.1007/978-981-97-5572-1_5(68-83)Online publication date: 31-Aug-2024
https://doi.org/10.1007/978-981-97-5572-1_5
Jing SChen LLi QWu D(2024)H$$^2$$GNN: Graph Neural Networks with Homophilic and Heterophilic Feature AggregationsDatabase Systems for Advanced Applications10.1007/978-981-97-5572-1_23(342-352)Online publication date: 31-Aug-2024
https://doi.org/10.1007/978-981-97-5572-1_23
Li XChen LWu D(2023)Adversary for Social Good: Leveraging Adversarial Attacks to Protect Personal Attribute PrivacyACM Transactions on Knowledge Discovery from Data10.1145/361409818:2(1-24)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3614098
Ashmore BChen LFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)HOVER: Homophilic Oversampling via Edge Removal for Class-Imbalanced Bot Detection on GraphsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615264(3728-3732)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615264
Li QChen LJing SWu D(2023)Pseudo-Labeling with Graph Active Learning for Few-shot Node Classification2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00133(1115-1120)Online publication date: 1-Dec-2023
https://doi.org/10.1109/ICDM58522.2023.00133
Li HHuang GLi YZhang XWang YLi J(2023)SEML: Self-Supervised Information-Enhanced Meta-learning for Few-Shot Text ClassificationInternational Journal of Computational Intelligence Systems10.1007/s44196-023-00287-616:1Online publication date: 1-Jul-2023
https://doi.org/10.1007/s44196-023-00287-6

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