Table 1.
Characteristics of recent work in Image-based personality analysis on social media.
Fig 1.
Overview of cross-modal analysis.
Fig 2.
Overview of cross-platform analysis.
Table 2.
Descriptive statistics of the PsychoFlickr data set.
Fig 3.
Distribution of different personality traits at the two data sets.
(a) Psycho-Flickr and (b) Cross-Linked Flickr and Twitter.
Table 3.
Descriptive statistics of the Cross-Linked Flickr and Twitter data set.
Table 4.
Description of features used in this work.
Fig 4.
Prediction performance of models trained on different features: color, CNN generic features (CNN_Gen), CNN object and scene categories (CNN_Obj) and Imagga tags; extracted from (a) profile images, (b) posted images and (c) liked images measured in Pearson correlation on the PsychoFlickr data set.
All Features denotes the performance of a model trained as linear ensemble of models trained on individual features. Significance of models is tested based on F-statistics (ANOVA); +: p < 0.05, *: p < 0.01, **: p < 0.001.
Fig 5.
Prediction performance of models trained on features extracted from profile, posted and liked images based on Pearson correlation on the PsychoFlickr data set.
Significance of models is tested based on F-statistics (ANOVA); +: p < 0.05, *: p < 0.01, **: p < 0.001.
Fig 6.
Comparison of models trained on text-predicted labels (Crossed-Link Flickr and Twitter) and those trained on survey label data at predicting survey labels (Psycho-Flickr dataset) using (a) color features (b) CNN generic Features (c) CNN Probabilities on ImageNet Scene and Object Categories.
Fig 7.
Comparison of models trained on text-predicted labels (Crossed-Link Flickr and Twitter) and those trained on survey label data at predicting survey labels (Psycho-Flickr dataset) using (a) color features (b) CNN generic Features (c) CNN Probabilities on ImageNet Scene and Object Categories.
Fig 8.
Comparison of models trained on text-predicted labels (Crossed-Link Flickr and Twitter) and those trained on survey label data at predicting survey labels (Psycho-Flickr dataset) using (a) color features (b) CNN generic Features (c) CNN Probabilities on ImageNet Scene and Object Categories.
Fig 9.
Prediction performance of different platforms for (a) profile, (b) posted, (c) liked images based on Pearson correlation on Cross-Linked Twitter and Flickr data set.
Significance of models is tested based on F-statistics (ANOVA); +: p < 0.05, *: p < 0.01, **: p < 0.001.
Fig 10.
Prediction performance of combining different modalities (i.e. profile pictures, posted and liked images) versus using each modality separately on (a) Flickr and (b) Twitter based on Pearson correlation on Cross-Linked Twitter and Flickr data set.
Combined Modality denotes the performance of a model trained as linear ensemble of models trained on individual modality. Significance of models is tested based on F-statistics (ANOVA); +: p < 0.05, *: p < 0.01, **: p < 0.001.
Fig 11.
Prediction performance for combining different modalities and different platforms based on Pearson correlation on Cross-Linked Twitter and Flickr data set.
Significance of models is tested based on F-statistics (ANOVA); +: p < 0.05, *: p < 0.01, **: p < 0.001.