Predicting and Characterising User Impact on Twitter

By Vasileios Lampos, Nikolaos Aletras, Daniel Preotiuc-Pietro, Trevor Cohn. 2014

83 citations recorded by the source corpus.

The open structure of online social networks and their uncurated nature give rise to problems of user credibility and influence. In this paper, we address the task of predicting the impact of Twitter users based only on features under their direct control, such as usage statistics and the text posted in their tweets.We approach the problem as regression and apply linear as well as non-linear learning methods to predict a user impact score, estimated by combining the numbers of the user's followers, followees and listings. The experimental results point out that a strong prediction performance is achieved, especially for models based on the Gaussian Processes framework. Hence, we can interpret various modelling components, transforming them into indirect 'suggestions' for impact boosting. © 2014 Association for Computational Linguistics.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

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Citations in the corpus

Non-Linear Regression for Bag-of-Words Data via Gaussian Process Latent Variable Set Model (2015, background): “2009) and data mining (Lampos and Aletras 2014).”
Analyzing Biases in Human Perception of User Age and Gender from Text (2016, background): “…political orientation (Volkova et al., 2014), income (Preot¸iuc-Pietro et al., 2015c), socio-economic status (Lam-pos et al., 2016), popularity (Lampos et al., 2014), personality (Schwartz et al., 2013) or mental illnesses (De Choudhury et al., 2013; Coppersmith et al., 2014; Preot¸iuc-Pietro…”
Beyond Binary Labels: Political Ideology Prediction of Twitter Users (2017, methodology): “…et al., 2010; Flekova et al., 2016b), gender (Burger et al., 2011; Sap et al., 2014), personality (Schwartz et al., 2013; Preoţiuc-Pietro et al., 2016), socioeconomic status (Preoţiuc-Pietro et al., 2015a,b; Liu et al., 2016c), popularity (Lampos et al., 2014) or location (Cheng et al., 2010).”
User-Level Race and Ethnicity Predictors from Twitter Text (2018, unknown): “We have tried other alternatives to building clusters: using other word similarities to generate clusters – such as NPMI (Lampos et al., 2014) or GloVe (Pennington et al., 2014) as proposed in (Preoţiuc-Pietro et al., 2015a) – or using standard topic modelling approached to create soft clusters of ”
Studying User Income through Language, Behaviour and Affect in Social Media (2015, methodology): “We have selected this configuration after trying several other clustering methods (i.e. LDA [37], GloVe embeddings [38], NPMI clustering [13]) and number of clusters (ranging from 30 to 2000).”
Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective (2016, background): “…is that it reduces the244 dimensionality of the feature space, which is essential for feature selection, it limits overfitting to245 training data (Lampos et al., 2014; Preoţiuc-Pietro et al., 2015; Preoţiuc-Pietro et al., 2015) and246 also provides a more concise semantic…”
Returning the N to NLP: Towards Contextually Personalized Classification Models (2020, background): “…ge-olocation (Eisenstein et al., 2010), political preferences (Volkova et al., 2014), socio-economic status (Preot¸iuc-Pietro et al., 2015), impact (Lampos et al., 2014), and a range of psychological traits and issues (Schwartz et al., 2013; Park et al., 2015; Sum-ner et al., 2012; Guntuku et…”
The role of personality, age, and gender in tweeting about mental illness (2015, background): “…in order to predict different user traits such as: age (Nguyen et al., 2011), gender (Sap et al., 2014), location (Cheng et al., 2010), impact (Lampos et al., 2014), political preference (Volkova et al., 2014), temporal orientation (Schwartz et al., 2015) or personality (Schwartz et al.,…”