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Found 7 papers, 2 papers with code
Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts
Tasks such as semantic search and clustering on crisis-related social media texts enhance our comprehension of crisis discourse, aiding decision-making and targeted interventions.
CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts
Additionally, we investigate the impact of model initialization on convergence and evaluate the significance of domain-specific models in generating semantically meaningful sentence embeddings.
Where did you tweet from? Inferring the origin locations of tweets based on contextual information
We employ a tweet contexualizer (locBERT) which is one of the core components of the proposed model, to investigate multiple tweets' distributions for understanding Twitter users' tweeting behavior in terms of mentioning origin and non-origin locations.
Socially Enhanced Situation Awareness from Microblogs using Artificial Intelligence: A Survey
The rise of social media platforms provides an unbounded, infinitely rich source of aggregate knowledge of the world around us, both historic and real-time, from a human perspective.
Twitter conversations predict the daily confirmed COVID-19 cases
Following the outbreak, the pandemic's seriousness has made people more active on social media, especially on the microblogging platforms such as Twitter and Weibo.
Determining Optimal Number of k-Clusters based on Predefined Level-of-Similarity
This paper proposes a centroid-based clustering algorithm which is capable of clustering data-points with n-features, without having to specify the number of clusters to be formed.
Predicting Outcome of Indian Premier League (IPL) Matches Using Machine Learning
Three of the trained models were seen to be correctly predicting more than 40 matches, with Multilayer Perceptron outperforming all other models with an impressive accuracy of 71. 66%.
