TY  - THES
A1  - Vasilev, Evgenii
T1  - Inferring gender of Reddit users
N2  - The content aggregator platform Reddit has established itself as one of the most popular websites in the world. However, scientific research on Reddit is hindered as Reddit allows (and even encourages) user anonymity, i.e., user profiles do not contain personal information such as the gender. Inferring the gender of users in large-scale could enable the analysis of gender-specific areas of interest, reactions to events, and behavioral patterns. In this direction, this thesis suggests a machine learning approach of estimating the gender of Reddit users. By exploiting specific conventions in parts of the website, we obtain a ground truth for more than 190 million comments of labeled users. This data is then used to train machine learning classifiers to use them to gain insights about the gender balance of particular subreddits and the platform in general. By comparing a variety of different approaches for classification algorithm, we find that character-level convolutional neural network achieves performance with an 82.3% F1 score on a task of predicting a gender of a user based on his/her comments. The score surpasses 85% mark for frequent users with more than 50 comments. Furthermore, we discover that female users are less active on Reddit platform, they write fewer comments and post in fewer subreddits on average, when compared to male users.
KW  - Natural Language Processing
KW  - Analysis of social platform
KW  - Reddit
KW  - Text classification
Y1  - 2018
UR  - https://kola.opus.hbz-nrw.de/frontdoor/index/index/docId/1619
UR  - https://nbn-resolving.org/urn:nbn:de:kola-16196
ER  -