Gender Detection on Social Networks Using Ensemble Deep Learning

Kowsari, Kamran; Heidarysafa, Mojtaba; Odukoya, Tolu; Potter, Philip; Barnes, Laura E.; Brown, Donald E.

doi:10.1007/978-3-030-63128-4_26

Cited by 12 publications

(8 citation statements)

References 28 publications

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“…Akbulut et al (2017) utilized knowledge from local receptive fields and CNNs to identify the gender of a person from their faces. Kowsari et al (2020) used an ensemble method to identify the gender from social networks. El-Sayed and Farouk (2020) identified gender from Egyptian Arabic dialect tweets using a variety of neural networks, including vanilla deep networks, CNNs, LSTM, GRU, both uni-directional and bidirectional.…”

Section: Related Workmentioning

confidence: 99%

A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework

Lokala¹,

Srivastava²,

Dastidar³

et al. 2022

Preprint

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Analyzing gender is critical to study mental health (MH) support in CVD (cardiovascular disease). The existing studies on using social media for extracting MH symptoms consider symptom detection and tend to ignore user context, disease, or gender. The current study aims to design and evaluate a system to capture how MH symptoms associated with CVD are expressed differently with the gender on social media. We observe that the reliable detection of MH symptoms expressed by persons with heart disease in user posts is challenging because of the co-existence of (dis)similar MH symptoms in one post and due to variation in the description of symptoms based on gender. We collect a corpus of 150k items (posts and comments) annotated using the subreddit labels and transfer learning approaches. We propose GeM, a novel taskadaptive multi-task learning approach to identify the MH symptoms in CVD patients based on gender. Specifically, we adapt a knowledgeassisted RoBERTa based bi-encoder model to capture CVD-related MH symptoms. Moreover, it enhances the reliability for differentiating the gender language in MH symptoms when compared to the state-of-art language models 1 . Our model achieves high (statistically significant) performance and predicts four labels of MH issues and two gender labels, which outperforms RoBERTa, improving the recall by 2.14% on the symptom identification task and by 2.55% on the gender identification task.

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Section: Related Workmentioning

confidence: 99%

A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework

Lokala¹,

Srivastava²,

Dastidar³

et al. 2022

Preprint

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“…In this way, we establish a level of differences between speeches used by MPs of a different gender. In gender detection, we find some interesting research that successfully applies machine learning and/or sentiment analysis (Argamon et al 2003;Park and Woo 2019;Menéndez, González-Barahona, and Robles 2020;Kowsari et al 2020). An important consideration in the prediction of speakers' gender is grammatical gender.…”

Section: Language and Gendermentioning

confidence: 99%

Multi-aspect Multilingual and Cross-lingual Parliamentary Speech Analysis

Miok¹,

Tenorio²,

Osenova³

et al. 2022

Preprint

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Parliamentary and legislative debate transcripts provide an exciting insight into elected politicians' opinions, positions, and policy preferences. They are interesting for political and social sciences as well as linguistics and natural language processing (NLP). Exiting research covers discussions within individual parliaments. In contrast, we apply advanced NLP methods to a joint and comparative analysis of six national parliaments (Bulgarian, Czech, French, Slovene, Spanish, and United Kingdom) between 2017 and 2020, whose transcripts are a part of the ParlaMint dataset collection. Using a uniform methodology, we analyze topics discussed, emotions, and sentiment. We assess if the age, gender, and political orientation of speakers can be detected from speeches. The results show some commonalities and many surprising differences among the analyzed countries.

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“…Along with detecting the possible connections between social factors and individual differences among language users, Author Profiling (AP) on social media has also become a prominent strand of sociolinguistic profiling, which has integrated Natural Language Processing techniques into producing profiles of news media writers. Linguistic features such as text string length and word frequency were used to construct computational models and build text classifiers (Peng et al, 2016;Manna et al, 2019;Kowsari et al, 2020). Multimodal texts including emails, microblogs, movie reviews, and online bulletin boards provided input data for text classifiers, which are capable of predicting text authors' age, gender, and first language background.…”

Section: The Construction Of Linguistic Profiles In Language Studiesmentioning

confidence: 99%

An investigation of high-proficiency L2 English speakers' oral test performance: A profiling approach

Gao

2022

Front. Commun.

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Linguistic profiles, which are often established through the measurement of linguistic features, are able to demonstrate characteristics shared by a specific type of text or a group of language learners. This paper examines the contexts and purposes related to profiling research in language studies, meanwhile synthesizing quantitative profiling methods such as cluster analysis, Principal Component Analysis (PCA), and Factor Analysis (FA). A profiling study of high-proficiency L2 English speakers' test performance is also presented, which explains the profiling procedure in L2 speaking assessment. Cluster analysis conducted on speech fluency and vocabulary variables rendered four different speech profiles, which are associated with the speakers' L1 background and L2 English proficiency level. This paper also discusses the interpretation of linguistic profiles, as well as the statistical concerns involved in the profile construction process.

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Gender Detection on Social Networks Using Ensemble Deep Learning

Cited by 12 publications

References 28 publications

A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework

A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework

Multi-aspect Multilingual and Cross-lingual Parliamentary Speech Analysis

An investigation of high-proficiency L2 English speakers' oral test performance: A profiling approach

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