Organized Event Participant Prediction Enhanced by Social Media Retweeting Data

• URL: http://arxiv.org/abs/2310.00896v1
• Date: Mon, 2 Oct 2023 04:26:07 GMT
• Title: Organized Event Participant Prediction Enhanced by Social Media Retweeting Data
• Authors: Yihong Zhang and Takahiro Hara
• Abstract summary: We propose to utilize social media retweeting activity data to enhance the learning of event participant prediction models. We conduct comprehensive experiments in two scenarios with real-world data.
• Score: 8.675064911866201
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Nowadays, many platforms on the Web offer organized events, allowing users to be organizers or participants. For such platforms, it is beneficial to predict potential event participants. Existing work on this problem tends to borrow recommendation techniques. However, compared to e-commerce items and purchases, events and participation are usually of a much smaller frequency, and the data may be insufficient to learn an accurate model. In this paper, we propose to utilize social media retweeting activity data to enhance the learning of event participant prediction models. We create a joint knowledge graph to bridge the social media and the target domain, assuming that event descriptions and tweets are written in the same language. Furthermore, we propose a learning model that utilizes retweeting information for the target domain prediction more effectively. We conduct comprehensive experiments in two scenarios with real-world data. In each scenario, we set up training data of different sizes, as well as warm and cold test cases. The evaluation results show that our approach consistently outperforms several baseline models, especially with the warm test cases, and when target domain data is limited.

Related papers

• Improving Event Definition Following For Zero-Shot Event Detection [66.27883872707523]
  Existing approaches on zero-shot event detection usually train models on datasets annotated with known event types. We aim to improve zero-shot event detection by training models to better follow event definitions.
  arXiv  Detail & Related papers  (2024-03-05T01:46:50Z)
• Evolving to the Future: Unseen Event Adaptive Fake News Detection on Social Media [29.22073623882667]
  We introduce Future ADaptive Event-based Fake news Detection (FADE) framework. We train a target predictor through an adaptive augmentation strategy and graph contrastive learning to make more robust overall predictions. We further mitigate event bias by subtracting the output of the event-only predictor from the output of the target predictor.
  arXiv  Detail & Related papers  (2024-02-29T06:40:53Z)
• CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification [51.58605842457186]
  We present a fine-grained disaster tweet classification model under the semi-supervised, few-shot learning setting. Our model, CrisisMatch, effectively classifies tweets into fine-grained classes of interest using few labeled data and large amounts of unlabeled data.
  arXiv  Detail & Related papers  (2023-10-23T07:01:09Z)
• Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting [74.68371461260946]
  SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
  arXiv  Detail & Related papers  (2023-10-20T06:17:02Z)
• Context-Based Tweet Engagement Prediction [0.0]
  This thesis investigates how well context alone may be used to predict tweet engagement likelihood. We employed the Spark engine on TU Wien's Little Big Data Cluster to create scalable data preprocessing, feature engineering, feature selection, and machine learning pipelines. We also found that factors such as the prediction algorithm, training dataset size, training dataset sampling method, and feature selection significantly affect the results.
  arXiv  Detail & Related papers  (2023-09-28T08:36:57Z)
• Harnessing the Power of Text-image Contrastive Models for Automatic Detection of Online Misinformation [50.46219766161111]
  We develop a self-learning model to explore the constrastive learning in the domain of misinformation identification. Our model shows the superior performance of non-matched image-text pair detection when the training data is insufficient.
  arXiv  Detail & Related papers  (2023-04-19T02:53:59Z)
• ASPEST: Bridging the Gap Between Active Learning and Selective Prediction [56.001808843574395]
  Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
  arXiv  Detail & Related papers  (2023-04-07T23:51:07Z)
• HumAID: Human-Annotated Disaster Incidents Data from Twitter with Deep Learning Benchmarks [5.937482215664902]
  Social media content is often too noisy for direct use in any application. It is important to filter, categorize, and concisely summarize the available content to facilitate effective consumption and decision-making. We present a new large-scale dataset with 77K human-labeled tweets, sampled from a pool of 24 million tweets across 19 disaster events.
  arXiv  Detail & Related papers  (2021-04-07T12:29:36Z)
• On Informative Tweet Identification For Tracking Mass Events [0.0]
  We investigate machine learning methods for automatically identifying informative tweets among those that are relevant to a target event. We propose a hybrid model that leverages both the handcrafted features and the automatically learned ones. Our experiments on several large datasets of real-world events show that the latter approaches significantly outperform the former.
  arXiv  Detail & Related papers  (2021-01-14T15:10:42Z)
• Event-Related Bias Removal for Real-time Disaster Events [67.2965372987723]
  Social media has become an important tool to share information about crisis events such as natural disasters and mass attacks. Detecting actionable posts that contain useful information requires rapid analysis of huge volume of data in real-time. We train an adversarial neural model to remove latent event-specific biases and improve the performance on tweet importance classification.
  arXiv  Detail & Related papers  (2020-11-02T02:03:07Z)
• Predicting event attendance exploring social influence [0.5134435281973136]
  We propose to model the social influence of friends on event attendance. We consider non-geotagged posts besides structures of social groups to infer users' attendance. The performance evaluation is conducted using two large music festivals datasets.
  arXiv  Detail & Related papers  (2020-02-16T20:03:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.