Gender Talk: Differences in Interaction Style in CMC

Sun, Xiaoning; Wiedenbeck, Susan; Chintakovid, Thippaya; Zhang, Qiping

doi:10.1007/978-3-540-74800-7_17

Cited by 3 publications

(3 citation statements)

References 1 publication

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“…Besides features derived from players' behaviour, we use a small set of relatively stable traits that can be collected outside of a game session through one-time self-reports. In particular, we added features based on previous work suggesting that perception of the other, e.g., trust, is affected by age [15], gender [19], the gender combination of people involved in an interaction [5,94,102,103], and personality traits like agreeableness or propensity to trust [32,79]. Finally, we use features based on identification as a gamer and preferred gaming style because of previous work suggesting a link between gaming frequency and social interaction [117].…”

Section: Potential Indicators Of Affiliationmentioning

confidence: 99%

Recognizing Affiliation: Using Behavioural Traces to Predict the Quality of Social Interactions in Online Games

Frommel

Sagl

Depping

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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Online social interactions in multiplayer games can be supportive and positive or toxic and harmful; however, few methods can easily assess interpersonal interaction quality in games. We use behavioural traces to predict affiliation between dyadic strangers, facilitated through their social interactions in an online gaming setting. We collected audio, video, in-game, and self-report data from 23 dyads, extracted 75 features, trained Random Forest and Support Vector Machine models, and evaluated their performance predicting binary (high/low) as well as continuous affiliation toward a partner. The models can predict both binary and continuous affiliation with up to 79.1% accuracy (F 1 ) and 20.1% explained variance (R 2 ) on unseen data, with features based on verbal communication demonstrating the highest potential. Our findings can inform the design of multiplayer games and game communities, and guide the development of systems for matchmaking and mitigating toxic behaviour in online games.

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Section: Potential Indicators Of Affiliationmentioning

confidence: 99%

Recognizing Affiliation: Using Behavioural Traces to Predict the Quality of Social Interactions in Online Games

Frommel

Sagl

Depping

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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“…Auch qualitative Analysen, die den Fokus auf ‚doing gender' legen, liegen inzwischen zahlreich für computervermittelte Kommunikation vor: So zeigen Thomson/Murachver/Green (2001) in zwei Akkomodationsexperimenten, dass sich in gemischten Gruppen die Sprachverwendung im Verlauf der Interaktion schnell angleicht und so kaum noch genderbezogene Unterschiede zu beobachten sind. Entsprechend müssen Studien wie die von Kommer (2008), Barrett/Lally (1999) oder Sun et al (2007), die genderbezogende Unterschiede belegen, mit Vorsicht betrachtet werden, denn dort werden oft nicht genügend Faktoren als Einflussvariablen berücksichtigt. In einer frühen Untersuchung von Weblogs von Herring/Paolillo (2006, S. 456) stellte sich nämlich heraus, dass sprachliche Merkmale nicht systematisch mit Gender korrelierten, dafür aber eindeutig mit Textsortenmerkmalen.…”

Section: Gender Und Cmcunclassified

♀ J = ♂ J? Oder: Das Gelächter der Geschlechter 2.0: Emojigebrauch in der WhatsApp-Kommunikation

Fladrich¹,

Imo²

2020

Deutsch in Sozialen Medien

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Praktiken des ‚doing', ‚undoing' und ‚indexing' von Gender finden sich auch in der computervermittelten Kommunikation, und es ist zu erwarten, dass sie sich dort ganz besonders im Gebrauch von Emojis niederschlagen. Zu erwarten ist dies, weil Emojis ein wichtiges Mittel zur Hervorbringung von Nähe, Emotionalität und Gruppenzugehörigkeit sind, und Gender ist ein Parameter, der bei diesen Aspekten eine Rolle spielt. In dem vorliegenden Beitrag soll auf der Basis der Mobile Communication Database 2 (MoCoDa2), einer Datenbank mit WhatsApp-Interaktionen, aus quantitativer und qualitativer Perspektive gefragt werden, welche Gemeinsamkeiten und Unterschiede sich im Emojigebrauch von Männern und Frauen finden lassen.

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“…Recommender systems are based on Machine-Learning algorithms that learn from the textual data generated by the users through comments and reviews or from their browsing activities in shopping applications or websites. In these marketing computational applications, gender has been considered a useful social variable [11]. For example, in [12], Aljohani and Cristea designed a Deep-Learning model to detect the gender of MOOC learners in order to offer them personalized course content.…”

Section: Introductionmentioning

confidence: 99%

A White-Box Sociolinguistic Model for Gender Detection

2022

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Within the area of Natural Language Processing, we approached the Author Profiling task as a text classification problem. Based on the author’s writing style, sociodemographic information, such as the author’s gender, age, or native language can be predicted. The exponential growth of user-generated data and the development of Machine-Learning techniques have led to significant advances in automatic gender detection. Unfortunately, gender detection models often become black-boxes in terms of interpretability. In this paper, we propose a tree-based computational model for gender detection made up of 198 features. Unlike the previous works on gender detection, we organized the features from a linguistic perspective into six categories: orthographic, morphological, lexical, syntactic, digital, and pragmatics-discursive. We implemented a Decision-Tree classifier to evaluate the performance of all feature combinations, and the experiments revealed that, on average, the classification accuracy increased up to 3.25% with the addition of feature sets. The maximum classification accuracy was reached by a three-level model that combined lexical, syntactic, and digital features. We present the most relevant features for gender detection according to the trees generated by the classifier and contextualize the significance of the computational results with the linguistic patterns defined by previous research in relation to gender.

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Gender Talk: Differences in Interaction Style in CMC

Cited by 3 publications

References 1 publication

Recognizing Affiliation: Using Behavioural Traces to Predict the Quality of Social Interactions in Online Games

Recognizing Affiliation: Using Behavioural Traces to Predict the Quality of Social Interactions in Online Games

♀ J = ♂ J? Oder: Das Gelächter der Geschlechter 2.0: Emojigebrauch in der WhatsApp-Kommunikation

A White-Box Sociolinguistic Model for Gender Detection

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