Neural Temporal Opinion Modelling for Opinion Prediction on Twitter

• URL: http://arxiv.org/abs/2005.13486v1
• Date: Wed, 27 May 2020 16:49:04 GMT
• Title: Neural Temporal Opinion Modelling for Opinion Prediction on Twitter
• Authors: Lixing Zhu and Yulan He and Deyu Zhou
• Abstract summary: We design a topic-driven attention mechanism to capture the dynamic topic shifts in the neighbourhood context. Experimental results show that the proposed model predicts both the posting time and the stance labels of future tweets more accurately than a number of competitive baselines.
• Score: 42.87769996249732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Opinion prediction on Twitter is challenging due to the transient nature of tweet content and neighbourhood context. In this paper, we model users' tweet posting behaviour as a temporal point process to jointly predict the posting time and the stance label of the next tweet given a user's historical tweet sequence and tweets posted by their neighbours. We design a topic-driven attention mechanism to capture the dynamic topic shifts in the neighbourhood context. Experimental results show that the proposed model predicts both the posting time and the stance labels of future tweets more accurately compared to a number of competitive baselines.

Related papers

• 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)
• Taureau: A Stock Market Movement Inference Framework Based on Twitter Sentiment Analysis [0.0]
  We propose Taureau, a framework that leverages Twitter sentiment analysis for predicting stock market movement. We first utilize Tweepy and getOldTweets to obtain historical tweets indicating public opinions for a set of top companies. We correlate the temporal dimensions of the obtained sentiment scores with monthly stock price movement data.
  arXiv  Detail & Related papers  (2023-03-30T19:12:08Z)
• A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction [100.9772316028191]
  In this paper, we experiment with a variety of adversarial attack configurations to fool three stock prediction victim models. Our results show that the proposed attack method can achieve consistent success rates and cause significant monetary loss in trading simulation.
  arXiv  Detail & Related papers  (2022-05-01T05:12:22Z)
• Joint Forecasting of Panoptic Segmentations with Difference Attention [72.03470153917189]
  We study a new panoptic segmentation forecasting model that jointly forecasts all object instances in a scene. We evaluate the proposed model on the Cityscapes and AIODrive datasets.
  arXiv  Detail & Related papers  (2022-04-14T17:59:32Z)
• Identification of Twitter Bots based on an Explainable ML Framework: the US 2020 Elections Case Study [72.61531092316092]
  This paper focuses on the design of a novel system for identifying Twitter bots based on labeled Twitter data. Supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm. Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions.
  arXiv  Detail & Related papers  (2021-12-08T14:12:24Z)
• You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction [52.442129609979794]
  Recent deep learning approaches for trajectory prediction show promising performance. It remains unclear which features such black-box models actually learn to use for making predictions. This paper proposes a procedure that quantifies the contributions of different cues to model performance.
  arXiv  Detail & Related papers  (2021-10-11T14:24:15Z)
• Understanding Information Spreading Mechanisms During COVID-19 Pandemic by Analyzing the Impact of Tweet Text and User Features for Retweet Prediction [6.658785818853953]
  COVID-19 has affected the world economy and the daily life routine of almost everyone. Social media platforms enable users to share information with other users who can reshare this information. We propose two CNN and RNN based models and evaluate the performance of these models on a publicly available TweetsCOV19 dataset.
  arXiv  Detail & Related papers  (2021-05-26T15:55:58Z)
• How Will Your Tweet Be Received? Predicting the Sentiment Polarity of Tweet Replies [3.5263924621989196]
  We propose a new task: predicting the predominant sentiment among (first-order) replies to a given tweet. We create RETWEET, a large dataset of tweets and replies manually annotated with sentiment labels. We use the automatically labeled data for supervised training of a neural network to predict reply sentiment from the original tweets.
  arXiv  Detail & Related papers  (2021-04-21T13:08:45Z)
• Choice-Aware User Engagement Modeling andOptimization on Social Media [0.9944647907864257]
  We formulate the engagement forecasting task as a multi-label classification problem. We propose a neural network architecture that incorporates user engagement history and predicts choice conditional on this context. We study the impact of recommend-ing tweets on engagement outcomes by solving an appropriately defined sweet optimization problem.
  arXiv  Detail & Related papers  (2021-04-01T23:31:01Z)
• Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction [57.56466850377598]
  Reasoning over visual data is a desirable capability for robotics and vision-based applications. In this paper, we present a framework on graph to uncover relationships in different objects in the scene for reasoning about pedestrian intent. Pedestrian intent, defined as the future action of crossing or not-crossing the street, is a very crucial piece of information for autonomous vehicles.
  arXiv  Detail & Related papers  (2020-02-20T18:50:44Z)

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.