Sentiment Analysis as an Indicator to Evaluate Gender disparity on Sexual Violence Tweets in South Africa

Oyasor, Jude Imuede; Raborife, Mpho; Ranchod, Pravesh

doi:10.1109/saupec/robmech/prasa48453.2020.9040955

Cited by 5 publications

(15 citation statements)

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“…3.2 Studies developing methods for gender and age prediction 3.2.1 Gender 44 studies developed ad-hoc methods to predict the Twitter user's gender. Of these, 32 predicted only gender 28,29,31,33,36,37,47,48,50,51,54,55,58,60,61,64,65,68,71,72,75,81,[83][84][85][86]90,92,94,96,100,101 and gender was predicted along with the user's age in 12 34,44,49,59,62,66,80,80,87,89,91,95 .…”

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

“…Most studies approached the problem of gender prediction as a binary classification task, predicting for an account the labels male or female, while three 72,91,98 added the classification of organization/brand. We found that approaches to predict gender covered multiple languages, including English 31,61,62,71,72,94,96 , German 55 , Slovenian 85 , Italian 28 , Japanese 68 , Arabic/Egyptian 36,37,58 , French, Dutch, Portuguese, Spanish and a multilingual study including 28 languages/dialects 91 .…”

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

“…For the training and validation of the approaches for gender detection, some studies used previously created annotated corpora, while others collected data directly from Twitter. Among the 19 studies that used previously annotated data sets, nine 34,36,37,47,49,65,66,75,100 used corpora from the PAN-CLEF author profiling tasks [102][103][104][105][106][107][108] , while ten studies 51,54,62,64,72,83,84,94,96,101 relied on data sets from other studies 92,[109][110][111][112][113][114][115] .…”

Section: Datasetsmentioning

confidence: 99%

“…Others may emulate human accounts and may be used with more malicious intent to sow discord, manipulate public opinion or spread misinformation. There were nine of the included studies 28,55,71,72,75,82,83,85,91 that removed non-personal organization accounts when they manually annotated their collections. Other studies implemented heuristics to explicitly detect and remove nonpersonal accounts 28,29,38,50,60,86,92,101 , bots 77 or both 58,116 .…”

Section: Non-personal Accountsmentioning

confidence: 99%

“…The data labeled with user's gender was used in the reviewed studies to build and evaluate classification models based on features describing the text in tweets (e.g., n-grams, word embeddings, hashtags, URLs) 36,37,44,[47][48][49][50]54,58,61,65,66,71,75,83,88,92,100 , or in the users' profile metadata (e.g., user names, bio, followers, users followed) 28,30,31,51,59,64,91,94,101 , a combination of their profile metadata and tweets 31,33,55,62,72,86,87,89,96 , images 31,59,87,91,95 . There was one study from Japan that included the user's geographic information under the assumption that, culturally, a person of a certain demographic is more likely to frequent specific places 68 .…”

Section: Features and Modelsmentioning

confidence: 99%

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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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Section: Characteristics Of Included Studiesmentioning

confidence: 99%

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Section: Non-personal Accountsmentioning

confidence: 99%

Section: Features and Modelsmentioning

confidence: 99%

See 3 more Smart Citations

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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A Novel Proof of Concept for Twitter Analytics Using Popular Hashtags: Experimentation and Evaluation

Ahire¹,

Bagul

Dhanawate³

et al. 2021

Proceedings of International Conference on Communication and Artificial Intelligence

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Methods and Annotated Data Sets Used to Predict the Gender and Age of Twitter Users: Scoping Review

O'Connor,

Golder,

Weissenbacher

et al. 2024

J Med Internet Res

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Background Patient health data collected from a variety of nontraditional resources, commonly referred to as real-world data, can be a key information source for health and social science research. Social media platforms, such as Twitter (Twitter, Inc), offer vast amounts of real-world data. An important aspect of incorporating social media data in scientific research is identifying the demographic characteristics of the users who posted those data. Age and gender are considered key demographics for assessing the representativeness of the sample and enable researchers to study subgroups and disparities effectively. However, deciphering the age and gender of social media users poses challenges. Objective This scoping review aims to summarize the existing literature on the prediction of the age and gender of Twitter users and provide an overview of the methods used. Methods We searched 15 electronic databases and carried out reference checking to identify relevant studies that met our inclusion criteria: studies that predicted the age or gender of Twitter users using computational methods. The screening process was performed independently by 2 researchers to ensure the accuracy and reliability of the included studies. Results Of the initial 684 studies retrieved, 74 (10.8%) studies met our inclusion criteria. Among these 74 studies, 42 (57%) focused on predicting gender, 8 (11%) focused on predicting age, and 24 (32%) predicted a combination of both age and gender. Gender prediction was predominantly approached as a binary classification task, with the reported performance of the methods ranging from 0.58 to 0.96 F1-score or 0.51 to 0.97 accuracy. Age prediction approaches varied in terms of classification groups, with a higher range of reported performance, ranging from 0.31 to 0.94 F1-score or 0.43 to 0.86 accuracy. The heterogeneous nature of the studies and the reporting of dissimilar performance metrics made it challenging to quantitatively synthesize results and draw definitive conclusions. Conclusions Our review found that although automated methods for predicting the age and gender of Twitter users have evolved to incorporate techniques such as deep neural networks, a significant proportion of the attempts rely on traditional machine learning methods, suggesting that there is potential to improve the performance of these tasks by using more advanced methods. Gender prediction has generally achieved a higher reported performance than age prediction. However, the lack of standardized reporting of performance metrics or standard annotated corpora to evaluate the methods used hinders any meaningful comparison of the approaches. Potential biases stemming from the collection and labeling of data used in the studies was identified as a problem, emphasizing the need for careful consideration and mitigation of biases in future studies. This scoping review provides valuable insights into the methods used for predicting the age and gender of Twitter users, along with the challenges and considerations associated with these methods.

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Sentiment Analysis as an Indicator to Evaluate Gender disparity on Sexual Violence Tweets in South Africa

Cited by 5 publications

References 14 publications

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

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

A Novel Proof of Concept for Twitter Analytics Using Popular Hashtags: Experimentation and Evaluation

Methods and Annotated Data Sets Used to Predict the Gender and Age of Twitter Users: Scoping Review

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