Probabilistic Inference of Twitter Users’ Age Based on What They Follow

Chamberlain, Benjamin Paul; Humby, Clive; Deisenroth, Marc Peter

doi:10.1007/978-3-319-71273-4_16

Cited by 21 publications

(26 citation statements)

References 10 publications

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“…Studies on demographics are also worth of note, in particular for those sites (e.g., Twitter) where demographic information is mostly unavailable. Chamberlain et al [50] and Zhang et al [51] successfully inferred users' age based on their interaction on the social media platform. Other studies on enhancing social data with demographic attributes include gender [52], location [53], ethnicity [54], and political affiliation [55].…”

Section: Related Workmentioning

confidence: 99%

TwitPersonality: Computing Personality Traits from Tweets Using Word Embeddings and Supervised Learning

et al. 2018

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Abstract:We are what we do, like, and say. Numerous research efforts have been pushed towards the automatic assessment of personality dimensions relying on a set of information gathered from social media platforms such as list of friends, interests of musics and movies, endorsements and likes an individual has ever performed. Turning this information into signals and giving them as inputs to supervised learning approaches has resulted in being particularly effective and accurate in computing personality traits and types. Despite the demonstrated accuracy of these approaches, the sheer amount of information needed to put in place such a methodology and access restrictions make them unfeasible to be used in a real usage scenario. In this paper, we propose a supervised learning approach to compute personality traits by only relying on what an individual tweets about publicly. The approach segments tweets in tokens, then it learns word vector representations as embeddings that are then used to feed a supervised learner classifier. We demonstrate the effectiveness of the approach by measuring the mean squared error of the learned model using an international benchmark of Facebook status updates. We also test the transfer learning predictive power of this model with an in-house built benchmark created by twenty four panelists who performed a state-of-the-art psychological survey and we observe a good conversion of the model while analyzing their Twitter posts towards the personality traits extracted from the survey.

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Section: Related Workmentioning

confidence: 99%

TwitPersonality: Computing Personality Traits from Tweets Using Word Embeddings and Supervised Learning

et al. 2018

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“…In our case, vertices represent Twitter users and edges represent a follower/followee relationship, and we treat edges as if they were undirected. This is justified because Twitter is predominantly an interest graph [14] and a large body of research has shown that the homophily principle applies to users who express similar interests in social networks [7,21,42]. By treating the graph as undirected we ensure that all users that follow a common account (indicative of an interest) have a maximum path distance of two.…”

Section: User Neural Graph Embeddingsmentioning

confidence: 99%

Predicting Twitter User Socioeconomic Attributes with Network and Language Information

Αλέτρας

Chamberlain

2018

Proceedings of the 29th on Hypertext and Social Media

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Inferring socioeconomic attributes of social media users such as occupation and income is an important problem in computational social science. Automated inference of such characteristics has applications in personalised recommender systems, targeted computational advertising and online political campaigning. While previous work has shown that language features can reliably predict socioeconomic attributes on Twitter, employing information coming from users' social networks has not yet been explored for such complex user characteristics. In this paper, we describe a method for predicting the occupational class and the income of Twitter users given information extracted from their extended networks by learning a low-dimensional vector representation of users, i.e. graph embeddings. We use this representation to train predictive models for occupational class and income. Results on two publicly available datasets show that our method consistently outperforms the state-of-the-art methods in both tasks. We also obtain further significant improvements when we combine graph embeddings with textual features, demonstrating that social network and language information are complementary.

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“…48,59,87,89,91,96 , the user's name 28,86,92 or declarations on other linked social media 95,96 . While three studies created their labeled datasets by using the accounts of famous social media influencers 57 or other unspecified collection of users whose gender is known 28,43 . Of the 24 studies, only 8 reported data availability with most 'by request', only 2 have working links to the whole corpus (SI Table S6).…”

Section: Datasetsmentioning

confidence: 99%

“…We found in those that developed ad hoc methods, 19 studies that sought to predict the Twitter user's age, where 7 predicted only age 32,43,45,52,69,73,74 . All but one of the studies 59 approached the detection of Twitter users' age as automatic classification of predefined age groups.…”

Section: Agementioning

confidence: 99%

Scoping Review of Methods and Annotated Datasets Used to Predict Gender and Age of Twitter Users

O’Connor

Golder

Weissenbacher

et al. 2022

Preprint

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Real World Data (RWD) has been identified as a key information source in health and social science research. An important, and readily available source of RWD is social media. Identifying the gender and age of the authors of social media posts is necessary for assessing the representativeness of the sample by these key demographics and enables researchers to study subgroups and disparities. However, deciphering the age and gender of social media users can be challenging. We present a scoping review of the literature and summarize the automated methods used to predict age and gender of Twitter users. We used a systematic search method to identify relevant literature, of which 74 met our inclusion criteria. We found that although methods to extract age and gender evolved over time to utilize deep neural networks, many still relied on more traditional machine learning methods. Gender prediction has achieved higher reported performance, while prediction of age performance lags, particularly for more granular age groups. However, the heterogeneous nature of the studies and the lack of consistent performance measures made it impossible to quantitively synthesize results. We found evidence that data bias is a prevalent problem and discuss suggestions to minimize it for future studies.

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Probabilistic Inference of Twitter Users’ Age Based on What They Follow

Cited by 21 publications

References 10 publications

TwitPersonality: Computing Personality Traits from Tweets Using Word Embeddings and Supervised Learning

TwitPersonality: Computing Personality Traits from Tweets Using Word Embeddings and Supervised Learning

Predicting Twitter User Socioeconomic Attributes with Network and Language Information

Scoping Review of Methods and Annotated Datasets Used to Predict Gender and Age of Twitter Users

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