Studying User Income through Language, Behaviour and Affect in Social Media

Preoţiuc-Pietro, Daniel; Volkova, Svitlana; Lampos, Vasileios; Bachrach, Yoram; Αλέτρας, Νικόλαος

doi:10.1371/journal.pone.0138717

Cited by 175 publications

(156 citation statements)

References 38 publications

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“…In summary the proposed approach is different from the previous works (Schwartz et al, 2015;Preoţiuc-Pietro et al, 2015;Park et al, 2017) in several ways. Unlike Schwartz et al (2015), we used a weakly supervised approach.…”

Section: Introductioncontrasting

confidence: 60%

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Temporal Orientation of Tweets for Predicting Income of Users

Hasanuzzaman¹,

Kamila²,

Kaur³

et al. 2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 2: Short Papers)

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Automatically estimating a user's socioeconomic profile from their language use in social media can significantly help social science research and various downstream applications ranging from business to politics. The current paper presents the first study where user cognitive structure is used to build a predictive model of income. In particular, we first develop a classifier using a weakly supervised learning framework to automatically time-tag tweets as past, present, or future. We quantify a user's overall temporal orientation based on their distribution of tweets, and use it to build a predictive model of income. Our analysis uncovers a correlation between future temporal orientation and income. Finally, we measure the predictive power of future temporal orientation on income by performing regression.

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Section: Introductioncontrasting

confidence: 60%

“…We used a dataset developed by Preoţiuc-Pietro et al (2015), which contains 5,191 Twitter users along with their platform statistics and ≈10 million historical tweets. The dataset is based on mapping a Twitter user to a job title and using this as a proxy for the mean income for that specific occupation.…”

Section: Income Data Of Usersmentioning

confidence: 99%

Temporal Orientation of Tweets for Predicting Income of Users

Hasanuzzaman¹,

Kamila²,

Kaur³

et al. 2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 2: Short Papers)

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“…Human beings' lightweight social grooming has evolved to adapt large groups, for example, gaze grooming (Kobayashi and Kohshima, 1997;Kobayashi and Hashiya, 2011) and gossip (Dunbar, 2004), because these grooming methods enable humans to have several social relationships and require less time and efforts. For example, in text communications over the Internet, such as on Twitter, users tend to construct wide and shallow social relationships that are used for acquiring and diffusing information (Arnaboldi et al, 2013c;Preoiuc-Pietro et al, 2015). Thus, lightweight social grooming is effective in constructing many weak social relationships.…”

Section: Discussionmentioning

confidence: 99%

Limitations of time resources in human relationships determine social structures

Takano¹,

Fukuda²

2017

Palgrave Commun

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The number of social relationships that a single human being can possibly be involved in is limited because individuals face time constraints (that is, time costs) in constructing and maintaining social relationships; furthermore, the distribution of the strength of such relationships (as measured by frequency of social interaction) looks significantly skewed (a power law distribution), that is, a few strong relationships and many weak relationships. This skewedness suggests that the costs and benefits of bonding with others depend on the strength of the social relationships: if it involves uniform costs and benefits, the distribution would not be skewed. The bonding is known as social grooming; that is, humans strategically construct their social relationships, and thus, complex human societies should also be strategically constructed. Therefore, it is important to know their strategies for understanding human societies. Previous studies provide evidence of social grooming strategies by examining the evolution and the difference between the various social grooming methods. However, quantitative laws that are important for theoretically understanding human societies are still open to investigation. Social big data is a particularly powerful tool for finding such laws. Therefore, we analyse data from six communication systems (Twitter, a social networking site providing two types of interactions, an avatar chat, a mobile phone and a short message service). We find a simple quantitative law by which social relationships are constrained Nm a (a41); here, N is the number of social relationships and m is a mean of the strength of those relationships. The fact that deep social relationships require higher costs per relationship than shallow relationships is suggested by a41 (if the both is equal then a will be 1), because the effect of m on the constraint increases with m. For exploring why a is greater than 1, we conduct an individual-based simulation where social grooming costs are assumed to increase linearly with the strength of social relationships. Our results indicate that this model fits all data sets; that is, it displays an explanation capacity for the phenomenon. In addition, an analysis of this simulation proves our assumption about the social grooming cost increasing with the strength of social relationships as being true. Moreover, it suggests that its gradient increases the width and shallowness of these relationships. The law and its causes suggest that mankind's evolution of social grooming has enabled changing social structures, and the phenomenon is because of the constraints of the social network generation. These findings will contribute towards an explanation of the evolution of the various social grooming methods of humans and their significantly large social group.

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“…Written text carries implicit information about the author, a relationship that has been exploited in natural language processing (NLP) to predict author characteristics, such as age (Goswami et al, 2009;Rosenthal and McKeown, 2011;Nguyen et al, 2011;Nguyen et al, 2014), gender (Sarawgi et al, 2011;Ciot et al, 2013;Liu and Ruths, 2013;Alowibdi et al, 2013;Volkova et al, 2015;Hovy, 2015), personality and stance (Schwartz et al, 2013b;Schwartz et al, 2013a;Volkova et al, 2014;Plank and Hovy, 2015;Preoţiuc-Pietro et al, 2015), or occupation (Preotiuc-Pietro et al, 2015a;Preoţiuc-Pietro et al, 2015b). The same signal has also been effectively used to predict mental health conditions, such as depression (Coppersmith et al, 2015b;Schwartz et al, 2014), suicidal ideation (Coppersmith et al, 2016;Huang et al, 2015), schizophrenia (Mitchell et al, 2015) or post-traumatic stress disorder (PTSD) (Pedersen, 2015), often more accurately than by traditional diagnoses.…”

Section: Introductionmentioning

confidence: 99%

Multitask Learning for Mental Health Conditions with Limited Social Media Data

Benton¹,

Mitchell²,

Hovy³

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

150

135

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Language contains information about the author's demographic attributes as well as their mental state, and has been successfully leveraged in NLP to predict either one alone. However, demographic attributes and mental states also interact with each other, and we are the first to demonstrate how to use them jointly to improve the prediction of mental health conditions across the board. We model the different conditions as tasks in a multitask learning (MTL) framework, and establish for the first time the potential of deep learning in the prediction of mental health from online user-generated text. The framework we propose significantly improves over all baselines and single-task models for predicting mental health conditions, with particularly significant gains for conditions with limited data. In addition, our best MTL model can predict the presence of conditions (neuroatypicality) more generally, further reducing the error of the strong feed-forward baseline.

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Studying User Income through Language, Behaviour and Affect in Social Media

Cited by 175 publications

References 38 publications

Temporal Orientation of Tweets for Predicting Income of Users

Temporal Orientation of Tweets for Predicting Income of Users

Limitations of time resources in human relationships determine social structures

Multitask Learning for Mental Health Conditions with Limited Social Media Data

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