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Found 12 papers, 0 papers with code
Event Detection from Social Media Stream: Methods, Datasets and Opportunities
Social media streams contain large and diverse amount of information, ranging from daily-life stories to the latest global and local events and news.
Rumor Detection on Social Media: Datasets, Methods and Opportunities
Social media platforms have been used for information and news gathering, and they are very valuable in many applications.
Uncover Sexual Harassment Patterns from Personal Stories by Joint Key Element Extraction and Categorization
Sexual harassment occurred in a variety of situations, and categorization of the stories and extraction of their key elements will provide great help for the related parties to understand and address sexual harassment.
Rumor Detection by Exploiting User Credibility Information, Attention and Multi-task Learning
In this study, we propose a new multi-task learning approach for rumor detection and stance classification tasks.
eventAI at SemEval-2019 Task 7: Rumor Detection on Social Media by Exploiting Content, User Credibility and Propagation Information
This paper describes our system for SemEval 2019 RumorEval: Determining rumor veracity and support for rumors (SemEval 2019 Task 7).
NAI-SEA at SemEval-2018 Task 5: An Event Search System
In this paper, we describe Alibaba{'}s participating system in the semEval-2018 Task5: Counting Events and Participants in the Long Tail.
Reuters Tracer: Toward Automated News Production Using Large Scale Social Media Data
It has a bottom-up approach to news detection, and does not rely on a predefined set of sources or subjects.
Social and Information Networks
Data Sets: Word Embeddings Learned from Tweets and General Data
In addition to the data sets learned from just tweet data, we also built embedding sets from the general data and the combination of tweets with the general data.
Learning Stock Market Sentiment Lexicon and Sentiment-Oriented Word Vector from StockTwits
Previous studies have shown that investor sentiment indicators can predict stock market change.
funSentiment at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from StockTwits and Twitter
This paper describes the approach we used for SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs.
funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts
This paper describes the approach we used for SemEval-2017 Task 4: Sentiment Analysis in Twitter.
