PANDORA Talks: Personality and Demographics on Reddit

Gjurković, Matej; Karan, Mladen; Vukojević, Iva; Bošnjak, Mihaela; Šnajder, Jan

doi:10.48550/arxiv.2004.04460

Cited by 7 publications

(12 citation statements)

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“…Jiang et al (2020) simply concatenate all the utterances from a single user into a document and encode it with BERT (Devlin et al, 2019) and RoBERTa (Liu et al, 2019). Gjurković et al (2020) first encode each post by BERT and then use CNN (LeCun et al, 1998) to aggregate the post representations. Most of them focus on how to obtain more effective contextual representations, with only several exceptions that try to introduce psycholinguistic features into DNNs, such as Majumder et al (2017) and Xue et al (2018).…”

Section: Personality Detectionmentioning

confidence: 99%

“…Personality detection can be formulated as a multidocument multi-label classification task (Lynn et al, 2020;Gjurković et al, 2020). Formally, each user has a set P = {p 1 , p 2 , .…”

Section: Our Approachmentioning

confidence: 99%

“…We choose two public MBTI datasets for evaluations, which have been widely used in recent studies (Tadesse et al, 2018;Hernandez and Knight, 2017;Majumder et al, 2017;Jiang et al, 2020;Gjurković et al, 2020). The Kaggle dataset 4 is collected from PersonalityCafe, 5 where people share their personality types and discussions about health, behavior, care, etc.…”

Section: Datasetsmentioning

confidence: 99%

“…As a major psycholinguistic instrument, Linguistic Inquiry and Word Count (LIWC) (Tausczik and Pennebaker, 2010) divides words into psychologically relevant categories (e.g., Function, Affect, and Social as shown in Figure 1) and is commonly used to extract psycholinguistic features in conventional methods (Golbeck et al, 2011;Sumner et al, 2012). Nevertheless, most recent works (Hernandez and Knight, 2017;Jiang et al, 2020;Keh et al, 2019;Lynn et al, 2020;Gjurković et al, 2020) tend to adopt deep neural networks (DNNs) to represent the posts and build predictive models in a data-driven manner. They first encode each post separately and then aggregate the post representations into a user representation.…”

Section: Introductionmentioning

confidence: 99%

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Psycholinguistic Tripartite Graph Network for Personality Detection

Yang¹,

Yang²,

Ouyang³

2021

Preprint

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Most of the recent work on personality detection from online posts adopts multifarious deep neural networks to represent the posts and builds predictive models in a data-driven manner, without the exploitation of psycholinguistic knowledge that may unveil the connections between one's language usage and his psychological traits. In this paper, we propose a psycholinguistic knowledge-based tripartite graph network, TrigNet, which consists of a tripartite graph network and a BERT-based graph initializer. The graph network injects structural psycholinguistic knowledge from LIWC, a computerized instrument for psycholinguistic analysis, by constructing a heterogeneous tripartite graph. The graph initializer is employed to provide initial embeddings for the graph nodes. To reduce the computational cost in graph learning, we further propose a novel flow graph attention network (GAT) that only transmits messages between neighboring parties in the tripartite graph. Benefiting from the tripartite graph, TrigNet can aggregate post information from a psychological perspective, which is a novel way of exploiting domain knowledge. Extensive experiments on two datasets show that TrigNet outperforms the existing state-of-art model by 3.47 and 2.10 points in average F1. Moreover, the flow GAT reduces the FLOPS and Memory measures by 38% and 32%, respectively, in comparison to the original GAT in our setting.

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Section: Personality Detectionmentioning

confidence: 99%

“…Personality detection can be formulated as a multidocument multi-label classification task (Lynn et al, 2020;Gjurković et al, 2020). Formally, each user has a set P = {p 1 , p 2 , .…”

Section: Our Approachmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Psycholinguistic Tripartite Graph Network for Personality Detection

Yang¹,

Yang²,

Ouyang³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Notably, there is a bias in those who use Reddit. In particular, while a global platform, its user base is largely North American [25], and so we should expect that these results are largely describing a North American experience of the virus. It is also clear that, similar to other forms of social media such as Twitter [26], there will also be significant demographic biases, on Reddit towards younger, more educated, politically-liberal, users [27].…”

Section: Limitationsmentioning

confidence: 99%

Symptom extraction from the narratives of personal experiences with COVID-19 on Reddit

Murray,

Mitchell,

Tuke

et al. 2020

Preprint

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Social media discussion of COVID-19 provides a rich source of information into how the virus affects people's lives that is qualitatively different from traditional public health datasets. In particular, when individuals self-report their experiences over the course of the virus on social media, it can allow for identification of the emotions each stage of symptoms engenders in the patient. Posts to the Reddit forum r/COVID19Positive contain first-hand accounts from COVID-19 positive patients, giving insight into personal struggles with the virus. These posts often feature a temporal structure indicating the number of days after developing symptoms the text refers to. Using topic modelling and sentiment analysis, we quantify the change in discussion of COVID-19 throughout individuals' experiences for the first 14 days since symptom onset. Discourse on early symptoms such as fever, cough, and sore throat was concentrated towards the beginning of the posts, while language indicating breathing issues peaked around ten days. Some conversation around critical cases was also identified and appeared at a roughly constant rate. We identified two clear clusters of positive and negative emotions associated with the evolution of these symptoms and mapped their relationships. Our results provide a perspective on the patient experience of COVID-19 that complements other medical data streams and can potentially reveal when mental health issues might appear. Keywords COVID-19 • topic modelling • networks • sentiment analysis • social media • Reddit Recent work in Natural Language Processing (NLP) has revealed various dynamics of the social media response to COVID-19. The majority of this work focuses on the discussion of COVID-19 on Twitter. Sha et al. [2] use topic modelling and network analysis to understand how decision-makers influence each other and identify leaders for different topics. Chen et al. [3] use sentiment analysis to indicate there may be racism behind controversial terms such 1 https://www.who.int/emergencies/diseases/novel-coronavirus-2019

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Multi-Document Transformer for Personality Detection

Yang

2021

AAAI

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Personality detection aims to identify the personality traits implied in social media posts. The core of this task is to put together information in multiple scattered posts to depict an overall personality profile for each user. Existing approaches either encode each post individually or assemble posts arbitrarily into a new document that can be encoded sequentially or hierarchically. While the first approach ignores the connection between posts, the second tends to introduce unnecessary post-order bias into posts. In this paper, we propose a multi-document Transformer, namely Transformer-MD, to tackle the above issues. When encoding each post, Transformer-MD allows access to information in the other posts of the user through Transformer-XL’s memory tokens which share the same position embedding.Besides, personality is usually defined along different traits and each trait may need to attend to different post information, which has rarely been touched by existing research. To address this concern, we propose a dimension attention mechanism on top of Transformer-MD to obtain trait-specific representations for multi-trait personality detection. We evaluate the proposed model on the Kaggle and Pandora MBTI datasets and the experimental results show that it compares favorably with baseline methods.

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PANDORA Talks: Personality and Demographics on Reddit

Cited by 7 publications

References 0 publications

Psycholinguistic Tripartite Graph Network for Personality Detection

Psycholinguistic Tripartite Graph Network for Personality Detection

Symptom extraction from the narratives of personal experiences with COVID-19 on Reddit

Multi-Document Transformer for Personality Detection

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