Table 1.
Schematic view of some relevant models in the last ten years for tweet, retweet, trend and hashtag prediction in Twitter
| References | Methodology | Main purpose |
|---|---|---|
| Ma (2012) and (2013) | Supervised Machine Learning | Prediction of hashtag popularity |
| Kupavski (2012) | Supervised Machine Learning | Retweet prediction |
| Kong (2014) | Supervised Machine Learning | Hashtag bursting prediction |
| Doong (2016) | Supervised Machine Learning | Prediction of hashtag popularity |
| Firdaus (2019) | Supervised Machine Learning | Retweet prediction |
| Zhang (2016) | Deep Learning and Neural networks | Retweet prediction |
| Yu (2020) | Deep Learning | Prediction of peak time for hashtag popularity |
| Pervin (2015) | Statistical analysis | Analysis of hashtag co-occurrence |
| Pancer and Poole (2016) | Hierarchical regression analysis | Prediction of links and retweets |
| Zhang (2013) | Non-linear auto-regression models | Trend prediction |
| Xiong (2012) | Epidemiological models | Modeling of information propagation |
| Jin et al. (2013) | Epidemiological models | Modeling of information cascades |
| Skaza and Blais (2017) | Epidemiological models | Modeling of hashtag dynamics |
| Kawamoto (2013) | Stochastic models and random processes | Modeling the dynamics of retweet activities |
| Ko et al. (2014) | Mathematical modeling | Modeling the propensity to tweet and retweet |
| Mollgaard (2015) | Stochastic and mathematical modeling | Modeling of tweet rates |
| Zhao (2015) | Mathematical and statistical modeling | Prediction of tweet popularity |
| Rizoiu et al. (2017) | Stochastic and mathematical modeling | Prediction of popularity for tweeted videos |
| Bao et al. (2019) | Mathematical modeling and supervised ML | Prediction of tweet popularity and retweets |