In this paper , we investigate the importance of social network information compared to content information in the prediction of a Twitter user 's occupational class . We show that the content information of a user 's tweets , the profile descriptions of a user 's follower / following community , and the user 's social network provide useful information for classifying a user 's occupational group . In our study , we extend an existing dataset for this problem , and we achieve significantly better performance by using social network homophily that has not been fully exploited in previous work . In our analysis , we found that by using the graph convolutional network to exploit social homophily , we can achieve competitive performance on this dataset with just a small fraction of the training data . * Equal Contribution ; work performed while both authors were visiting Singapore University of Technology and Design ( SUTD ) . T1 : Day at the races . T2 : The new pitch era starts here . Groundsman . Wolves fan . Horse racing enthusiast . A Commercial and Domestic Grounds Maintenance company T1 : Pitch at wembley looks great . T2 : Spurs will have best stadium in uk . Twitter ( http://twitter.com ) is a microblogging service launched in 2006 , where , a user can publish messages with up to 280 characters , called " tweets " . Unlike many other social networking platforms , such as Facebook and LinkedIn , Twitter does not provide structured fields for users to fill in personal information . However , a user can write a 160-character-long small public summary about itself called a " Bio " . Besides linguistic information from tweets and Bios , online social media is a rich source of network information . People 's personal networks are homogeneous , i.e. , friends share more attributes such as race , ethnicity , religion , and occupation-known as the homophily principle ( McPherson et al . , 2001 ) . Such network information has been utilized in friend recommendation ( Guy et al . , 2010 ) , community detection Follow Figure 1 : User and Network information on Twitter Microblog . ( Yang and Leskovec , 2013 ) , etc . Figure 1 shows two users connected on Twitter . By looking at their Bio and tweets , it can be inferred that these users share the same occupational interest . Profiling users can enhance service quality and improve product recommendation , and hence is a widely studied problem . User occupational class prediction is an important component of user profiling and a sub-task of user demographic feature prediction . Existing approaches to predicting Twitter users ' demographic attributes explore , select , and combine various features generated from text and network to achieve the best predictive performances in respective classification tasks ( Han et al . , 2013 ; Miller et al . , 2012 ; Preot ¸iuc-Pietro et al . , 2015 ; Huang et al . , 2015 ; Aletras and Chamberlain , 2018 ) . The three categories of features are : account level features , tweet text features , and network based features . Past research have shown the distinctive usage of language across gender , age , location , etc . in tweets ( Sloan et al . , 2015 ; Cheng et al . , 2010 ; Burger et al . , 2011 ; Rao et al . , 2010 ) , which makes content based prediction effective . As for user occupational class prediction , Preot ¸iuc-Pietro et al . ( 2015 ) built a dataset where users are assigned to hierarchical job categories . They used word cluster distribution features of content information to predict a user 's occupational group . Aletras and Chamberlain ( 2018 ) constructed a user 's followings connections to learn the user embedding as a feature input to the classification models . Considering the regional disparities of economic development stages , the major job categories may vary significantly across regions . Sloan et al . ( 2015 ) summarized occupation distribution of Twitter users in the UK by looking into their profiles . In this paper , we analyze the usefulness of a user 's network information over the user 's tweets for predicting its occupational group . We extend the existing dataset for occupation classification ( Preot ¸iuc-Pietro et al . ( 2015 ) ) by introducing the network information about a user , i.e. follower / following IDs together with their Bio descriptions , and we construct a user-centric network to extract useful community and text based features . The acquired features from the network are then exploited using a graph neural network . The obtained results show the importance of a network information over tweet information from a user for such a task . Previous works have used tweets or a fraction of the network information to extract features for occupation classification . To analyze the importance of network information , we extended an existing Twitter dataset for a user 's social media connections ( follow information ) . We showed that by using only follow information as an input to graph convolutional networks , one can achieve a significantly higher accuracy on the prediction task as compared to the existing approaches utilizing tweet-only information or partial network structure . Directions of future research include adaptation of our methods to a large scale , sparsely connected social network . One might also want to investigate the inductive settings of GCN ( Hamilton et al . , 2017 ) to predict demographic information of a user from outside the black network .
Existing systems only use limited information from the tweets network to perform occupation classification.
They extend existing Twitter occupation classification graph-based models to exploit content information by adding textual data to existing datasets.
They show that textual feature enables graph neural networks to predict Twitter user occupation well even with a limited amount of training data.