A machine learning approach for gender identification of Greek tweet authors

Baxevanakis, Spiros; Gavras, Stelios; Mouratidis, Despoina; Kermanidis, Katia Lida

doi:10.1145/3389189.3397992

Cited by 8 publications

(17 citation statements)

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“…The identification of patterns in data has been a demanding task in the context of social text, mainly due to its unstructured nature, rich morphology and increasing volume [2,4,7,10,11,[13][14][15][16]. Several researchers have focused on the identification of linguistic and/or behavioral patterns of interest in social text data.…”

Section: Linguistic and Behavioral Patternsmentioning

confidence: 99%

“…Additionally, there are certain approaches that have explored argument extraction and detection from text corpora [13][14][15]. Another approach attempted authorship attribution and author's gender identification for bloggers [10,11]. An overview of the recent literature regarding linguistic patterns analysis, which is discussed in this subsection, is shown in Tables 1 and 2.…”

Section: Linguistic Patterns Analysismentioning

confidence: 99%

“…Baxevanakis et al [11] created a novel, manually annotated, social text corpus 18 written in Modern Greek and presented an NLP framework for gender identification of the author. The data originated from a random population of Twitter users; 463 authors with 99.023 mean number of tweets per author, resulting in 45,848 tweets.…”

Section: Authorship Attribution and Gender Identificationmentioning

confidence: 99%

“…The contribution of the works analyzed in our paper is significant for text mining and NLP tasks in Modern Greek social text. Regarding linguistic patterns analysis [2,7,[10][11][12][13][14][15]37], several data sets and corpora were created, a POS tagger, a POS tag set and a bidialectal classifier were introduced, several methods for sentiment intensity analysis, machine translation engines, argument extraction authorship and gender attribution were presented, and the most determinant features for bidialectal classifying, argument extraction and authorship and gender attribution were extracted.…”

Section: Approaches On Developing Modern Greek Social Web Text Data Sets and On Modernmentioning

confidence: 99%

“…The increasing number of users connecting through social networks and web platforms, such as Facebook and Twitter, as well as numerous Blogs and Wikis, creates continuously a significant volume in written communication through the Web [1][2][3][4][5][6][7]. The amount and quality of information and knowledge extracted from social text has been considered crucial to studying and analyzing public opinion [1,3,5,8,9], as well as linguistic [2,7,[10][11][12][13][14][15] and behavioral [4,6,[16][17][18] patterns. In its typical form, social text is often short in length, low in readability scores, informal, syntactically unstructured, characterized by great morphological diversity and features of oral speech, misspellings and slang vocabulary, consequently presenting major challenges for NLP and Data Mining tasks [2,4,7,10,11,13,[13][14][15][16]19].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

The Modern Greek Language on the Social Web: A Survey of Data Sets and Mining Applications

Nikiforos

Voutos

Drougani

et al. 2021

Data

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Mining social web text has been at the heart of the Natural Language Processing and Data Mining research community in the last 15 years. Though most of the reported work is on widely spoken languages, such as English, the significance of approaches that deal with less commonly spoken languages, such as Greek, is evident for reasons of preserving and documenting minority languages, cultural and ethnic diversity, and identifying intercultural similarities and differences. The present work aims at identifying, documenting and comparing social text data sets, as well as mining techniques and applications on social web text that target Modern Greek, focusing on the arising challenges and the potential for future research in the specific less widely spoken language.

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Section: Linguistic and Behavioral Patternsmentioning

confidence: 99%

Section: Linguistic Patterns Analysismentioning

confidence: 99%

Section: Authorship Attribution and Gender Identificationmentioning

confidence: 99%

Section: Approaches On Developing Modern Greek Social Web Text Data Sets and On Modernmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Modern Greek Language on the Social Web: A Survey of Data Sets and Mining Applications

Nikiforos

Voutos

Drougani

et al. 2021

Data

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Author Gender Identification for Urdu Articles

Sarwar

2022

Lecture Notes in Computer Science

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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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A machine learning approach for gender identification of Greek tweet authors

Cited by 8 publications

References 4 publications

The Modern Greek Language on the Social Web: A Survey of Data Sets and Mining Applications

The Modern Greek Language on the Social Web: A Survey of Data Sets and Mining Applications

Author Gender Identification for Urdu Articles

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

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