Table 1 Unsupervised learning approaches used for event detection
From: Real-time event detection in social media streams through semantic analysis of noisy terms
References | Data sources/ Features | Algorithms | Inclusion of local vocabulary | Treatment of Slang, Acronym, and Abbreviation (SAB) |
|---|---|---|---|---|
[14] | Twitter/ Textual | LSH, Shannon entropy | No | No |
[20] | Twitter/ Textual | EDCoW, Term Frequency | No | No |
[21] | Twitter/ Textual | Term Frequency, Kullback–Leibler divergence | No | No |
[15] | Twitter/ Textual | Similarity score, Cluster summarization | No | No |
[26] | Twitter/ Textual | Fuzzy Hierarchical, Agglomerative Clustering | No | No |
[24] | Twitter/ Textual, Spatiote-mporal | TF-IDF, Normalised Mutual Information Frequency | No | No |
[16] | Twitter/ Textual | Named entity, TF-IDF, Sigma rule | No | No |
[23] | Twitter/ Textual, Spatio-temporal | BIRCH | No | No |
[25] | Twitter/ Textual | OPTICS | No | No |
[27] | Twitter/ Textual, Spatiote-mporal | Wavelet decomposition, Modularity-based clustering | No | No |
Weibo, Twitter/ Textual | Expected Maximization, MapReduce, K-means, Hierarchical agglomerative | No | No | |
[29] | Twitter, Flickr, YouTube/Textual | Incremental TF-IDF, Skewness, Learn and Forget term selection, Growing, Gaussian Mixture Model | No | No |
[12] | Twitter, Tumblr/ Textual | Time-evolving graphs | No | No |
[30] | Twitter/ Textual | Longest Common, Subsequence, Incremental clustering | No | No |
[31] | Twitter/ Textual | Entity co-occurrence, Louvain clustering, Aggregate ranking | No | No |
[32] | Twitter/Multimedia | Incremental clustering, Influence maximization algorithm | No | No |
[33] | Twitter, Facebook, Weibo/Textual | Indexed based algorithm | No | No |
[35] | Twitter, Weibo/Textual | Sub-event representation learning | No | No |
[36] | Twitter/Textual | Spatiotemporal clustering | No | No |