=Paper=
{{Paper
|id=Vol-2842/paper_7
|storemode=property
|title=Age Determination of the Social Media Post's Author Using Deep Neural Networks and Facial Processing Models
|pdfUrl=https://ceur-ws.org/Vol-2842/paper_6.pdf
|volume=Vol-2842
|authors=Aleksandr Romanov,Anna Kurtukova,Artem Sobolev,Anastasia Fedotova
}}
==Age Determination of the Social Media Post's Author Using Deep Neural Networks and Facial Processing Models==
https://ceur-ws.org/Vol-2842/paper_6.pdf
Age Determination of the Social Media Post's Author Using
Deep Neural Networks and Facial Processing Models
Aleksandr Romanov a, Anna Kurtukova a, Artem Sobolev a and Anastasia Fedotova a
a
Tomsk State University of Control Systems and Radioelectronics (TUSUR), 146, Red Army street Tomsk,
634045, Russia
Abstract
This article describes an original approach based on deep neural network models to the
problem of determining the age of the text author. A detailed description of the author's
method and the results of its application is demonstrated. The analysis of tools for filtration
of unreliable data in photo age determination, methods of text author's age determination are
also present in the paper. This method can be considered a deserving competitor for other
approaches due to the obtained result - 82% accuracy.
Keywords 1
Authorship, age, text analysis, computer vision, social networks, FastText, VGG-Face,
CRNN.
1. Introduction
This research is indirectly related to intellectual analysis of the text [1-4], establishing the
authorship of the text [5, 6], and tonal analysis. This fact establishes the high relevance of the topic.
The most informative attributes of attribution are the author's gender and age. Their use makes it
possible to effectively separate the true / false authors of the text, as well as to increase the separating
ability of the model in classification.
Possible applications include information security, science and commerce (e.g., as a way to
optimize targeted advertising) and science (as a tool for linguistic research), and forensic science. In
relation to the latter, it is important to differentiate users of social networks in order to restrict children
from adult content and to prevent threats of pedophilia.
In addition, solving these issues is very important for the field of forensic science in particular.
detection of the author of an anonymous note with threats;
Age-based differentiation of social media users to counter pedophilia and prevent children from
accessing adult content;
identification of the author of the suicide note, if it is necessary to confirm the version of
suicide.
Everyone has a unique writing style. The author's style consists in a variety of vocabulary, speech
circulation, building a sentence structure, vocabulary, using certain linguistic structures. Such features
make it possible to differentiate people into groups by age.
YRID-2020: International Workshop on Data Mining and Knowledge Engineering, October 15-16, 2020, Stavropol, Russia
EMAIL: alexx.romanov@gmail.com (Aleksandr Romanov); av.kurtukova@gmail.com (Anna Kurtukova); bingjo-ya@yandex.ru (Artem
Sobolev); afedotowaa@icloud.com (Anastasia Fedotova)
ORCID: 0000-0002-2587-2222 (Aleksandr Romanov); 0000-0001-5619-1836 (Anna Kurtukova); 0000-0002-2193-5994 (Artem Sobolev);
0000-0001-7844-4363 (Anastasia Fedotova)
©️ 2020 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
51
2. Purpose and objectives of the research
The purpose of the study is to determine the age of the author based on an anonymous text.
Designated tasks: collecting data from a social network, pre-processing of texts, study and analysis of
text's classification methods, determination of age from a photo, choice of methods and algorithms,
data filtering using CV technologies, classification of the original and processed data, analysis of the
results.
Ultimately, the use of CV technologies for data filtering gave a result, the classification accuracy
increased by almost 20%
3. Literature review
Many studies are devoted to determining the age of the author of the text. In [7], four machine
learning algorithms (ml): logistic regression, naive Bayesian classifier (NB), gradient boosting, and
multi-layer perceptron (MLP) were used as classifiers. The data for the study consisted of 350 texts
divided into 3 classes (15-19, 20-24, 25, and older). The best result was obtained when using MLP.
The accuracy was 67.18%. The average accuracy of the ensemble with the used classifiers was 51%.
The TweetGenie system for determining the user's age is presented in the article [8]. The system
combines sociolinguistics ideas and data obtained from social networks user’s pages in social
networks. The corpus was based on the tweets of 3,000 users. The authors have identified 3 categories
of users (under 20, from 20 to 40 and over 40 years). The mean absolute error (MAE) for this system
was 4.844, the Pearson coefficient was 0.866. The results show the limitations of current text-based
age-setting approaches. Considering this aspect as a fixed variable ignores the variety of ways in
which a user is unique in natural language. A disadvantage of the system is the tendency to
underestimate the age of older people: Twitter users are perceived to be younger than they are.
The article [9] is devoted to determining the age of the text author using the Support Vector
Machine (SVM). Texts from social networks were used in the study. The authors divided user posts
into different groups. Short texts from 12 tokens were used in the experiments. For the class "16+
years", the accuracy was 71.3%, for the class "18+ years" - 80.8%, and for the class "25+ years" -
88.2%. n-gram words were the best signs for age classification in this study.
The authors of [10] offer several classification models, using methods of deep learning and
classical machine learning. These methods determine the age of the Twitter user based on the text
content of tweets. The age classifier was trained on a data set of 11160 users, which includes
combinations of words and symbolic n-grams as attributes derived from Latent semantic analysis
(LSA). When solving the problem of author's profiling in the framework of PAN-2018 (international
competition on tasks of digital text forensics and stylometry), this model reached the best accuracy -
82.21% for the English language.
Article [11] is devoted to the comparison of SVM and Bayesian neural network (NN). The texts
for the experiments were obtained from online diaries and divided into 4 age categories: up to 18, 20-
27, 30-37, 40 and more years. The accuracy obtained by the SVM model was 50.1%, and the
Bayesian NN — 48.2%.
Many of the previously mentioned methods for determining the age of the author of a text have
low accuracy. Another drawback is their strict length dependency. They are oriented either to short
texts (posts in social networks and blogs, comments, etc.) or to long texts (articles, posts, reviews,
etc.). This approach is wrong, as it does not allow to estimate the universality of the model when
solving real problems. The slang that users use in their posts is often not structured and contains
noise. Intentional and unintentional illiteracy also makes classification difficult. The main part of the
described shortcomings can be eliminated at the stage of text preprocessing, but bringing the data into
an appropriate form does not guarantee high accuracy. Many factors affect the accuracy of a model.
The most significant of these is the original data. If they are unreliable, noisy, or unbalanced, training
the model will not be effective enough. For example, real users of social networks may deliberately
indicate the wrong age in their profile. This requires filtering the data. The solution to this problem
can be the use of computer vision (CV) models intended for the related problem of determining the
age of a user from a photograph. The overwhelming majority of approaches to its solution are based
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on convolutional neural network (CNN) architectures and their more complex modifications. Let's
consider in more detail the most effective of them.
The work [12] is based on VGG-16 architecture and the Deep EXpectacion (DEX) method. The
essence of the method consists in detecting a face in a photo, for which an ensemble of 20 different
architectures is used to predict age. As experimental data we used celebrity photos from IMDB and
Wikipedia. DEX method showed the best result in the ChaLearn Looking at People contest in 2015,
and ε-error was only 0.264975.
In [13] SVM approach was used. The approach is inspired by the success of deep NN [14, 15],
where the problem of retraining can be solved by adding a “dropout” regularization function that turns
off some part of the neurons during training. This approach improves the adaptation of neurons to
input data. The accuracy of the face images collected by the authors reaches 70%.
A new CNN model, Soft Stagewise Regression Network (SSR-Net), is presented in [16]. Age
determination using a multi-class classifier is performed in several stages. Each stage of age
determination is responsible for refining the results of the previous one, which allows for a more
accurate assessment. To solve the task of quantization of age grouping by classes, SSSR-Net assigns a
dynamic range to each age class, allowing it to shift and scale according to the input image. The main
advantage of the obtained model is its compactness - it takes only 0.32 MB. Despite this, SSR-Net
performance is close to modern methods, the size of the models which are often 1500 times larger.
The MAE of this model was 2.52.
The study [17] is based on the hyperplane ranking algorithm. In the proposed approach, age labels
are used to predict rank. Age rank is obtained by aggregating a series of binary classification results.
The FG-NET dataset was used for the experiments. The results showed that the learning strategy
chosen by the authors exceeded the traditional approaches of classification, regression and ranking.
The best results of the system are shown in images with neutral facial expression, emotions influence
the results of age assessment to the detriment. The MAE value for this approach was 3.82.
The article [18] shows the VGG-Face model developed on the basis of the well-known VGG-
Very-Deep-16 architecture. The performance of the model was estimated on marked faces in the wild
[19] and YouTube Faces [20]. The obtained accuracy was 98%. In this paper it is proposed to refer to
the experience of foreign researchers and to use the advantages of NLP and CV models to solve the
problem of determining the age of the author of the Russian-language text.
Comparison of the results presented in works devoted to this topic is shown in Table 1.
Table 1
Comparison of methods proposed in works
Method Accuracy±Std(%)
MLP [7] 67.18
SVM [9] 71.3, 80.8, 88.2
CNN [10] 82.21
SVM, Bayesian NN [11] 50.1, 48.2
SVM [13] 70
DeepFace[21] 97.35±0.25
VGG-Very-Deep-16 [18] 98
light-CNN [27] 98.8
DeepID2 [22] 99.15±0.13
DeepID3 [23] 99.53±0.10
FaceNet [24] 99.63±0.09
Baidu [25] 99.77
VGGface [26] 98.95
light-CNN [27] 98.8
Center Loss [28] 99.28
L-softmax [29] 98.71
Range Loss [30] 99.52
L2-softmax [31] 99.78
Normface [32] 99.19
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CoCo loss [33] 99.86
vMF loss [34] 99.58
Marginal Loss [35] 99.48
SphereFace [36] 99.42
CCL [37] 99.12
AMS loss [38] 99.12
Cosface [39] 99.33
Arcface [40] 99.83
Ring loss [41] 99.50
4. Methodology
The technique for determining the age of the text’s author presented in Figure 1 includes several
stages.
1. Clearing text from the excessive and disturbing data is performed during pre-processing. Noises
make it difficult to perform classification. Spam messages are often found in social networks, which
is why texts are cleared of such spam words as "asset", "subscription", "like", "hacking", "mutual
subscription", as well as from duplicates. Another characteristic feature of the online platform texts is
the frequent use of emoticons, in particular by the audience under 18 years of age. All emoticons are
replaced with the "@emoji". Short comments that consist mainly of emoticons and include less than 5
Russian words should also be deleted, as they are not informative enough.
2. For additional data verification, a filtering step is required. Often, users of social networks
indicate the wrong age in their profile. This may be due to various reasons; however, this is mostly
done for the purpose of accessing 18 or more content or registering directly on an online platform. As
a tool for effective filtering of inaccurate data, it was decided to use photos from users ' pages in the
social network and a CV model.
According to the results obtained by other researchers, the VGG-Face is the most suitable model
for solving the problem of determining age from a user's photo. Therefore, it was decided to use it as
the basis for filtering inaccurate data method that includes several steps: the user's age is determined
by the photo, then 2 years are added/subtracted to it. If the age specified by the user falls within the
interval, then the counter increases, differently it remains unchanged. The age of the user is
considered correct if the counter is equal to or more than half the number of the author’s photos.
3. Using the data filtered in the previous stage is necessary for performing the deep neural
network training stage. The task of dividing texts into age groups is not trivial, so a separate
preliminary study was devoted to it [42], aimed at identifying the most effective architecture of neural
networks. It was found that FastText and CRNN [43] - a hybrid of CNN and recurrent neural network
(RNN) are the most effective architectures for determining the author's age from the text.
Skip-gram with negative sampling is used for vector representation of words. Negative sampling
provides negative examples — to connect words that are not pairs in context. For one word, from 3 to
20 negative words are selected. Skip-gram ignores word structure, so a model was added that breaks
words into n-grams. Usually, the value of n can be from 3 to 6. The full word is also written to the
chain of n-grams. This approach allows to work with words that the model has never met before.
Hashing was used to process a large dimension of features obtained by splitting into n-grams.
Recurrent layers respond to temporary changes and correct contextual dependencies. The CNN
architecture is an effective combination for analyzing text sequences; the convolutions of various
dimensions available in it make it possible to select the most significant features for classification,
regardless of their size.
4. The final stage includes the validation of the selected NN models. The procedure for validation
was cross-checking for 10 blocks. It is used as a way to get a reliable assessment and improve the
learning process. The most accurate model based on validation results is chosen as a decision-making
tool.
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Figure 1: The technique for determining the age of the text’s author
5. Results
To carry out an effective classification of texts, a large set of representative data is required. There
is no set of marked-up texts for solving the problem of determining the age of the author of a Russian-
language text. Therefore, such a data set was formed and marked up on its own.
Since countering pedophilia in social networks is one of the key areas of application of solutions to
this problem, we used real data from users of The Vkontakte online platform. On average, the most
popular social network in Russia and the CIS, Vkontakte, the number of daily messages reaches 550
thousand messages left by more than 30 thousand users. Data was collected from community pages
using the API. Thus, more than 50 thousand links and 70 thousand photos were received. The data
collection process was automated - a special script extracted the last 100 entries left on the community
pages and their accompanying comments. The total amount of data received was more than 2 million
records. Each of these entries includes: a short comment, 5 photos from the author's account, and a tag
containing information about the age of the comment's author.
The data obtained by the described method was pre-processed according to the author's method:
cleared of spam, uninformative messages, duplicates, etc. The preprocessed data was then filtered by
the VGG-Face model. As a result of applying the data filtering method based on the G-Face model,
5.5 thousand texts were selected out of 75 thousand texts where the user's age was reliable.
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In this experiment, distributions of VK authors by age before and after filtering (Figure 2) was
obtained. The graph shows a drop in the share of users under the age of 18 and an increase in the
share of users over the age of 18. Based on the obtained distributions, we can conclude that some
users deliberately underestimate their age in the profile.
Figure 2: Distribution of users by age before and after filtering
Two data sets were created for model training. The first set included texts, age marks and images
that had not been filtered using the VG-Face method, while the second set included filtered data
respectively. It was decided to implement both binary and multiclass classification. For this purpose,
the data contained in the set were divided into categories. In the first case, the sets included two
categories: up to 18 years old and over 21 years old, in the second set - up to 18 years old, from 21 to
27 years old and over 30 years old. The choice of categories is related to the need to distinguish
between juvenile and adult users. It should be noted that the texts written by authors aged 18 to 21
years were not taken into account when teaching the models, because the separation ability of the
models in this interval is unsatisfactory. This has a negative impact on the end result. The situation is
similar to the 27-30 years interval. The main distinguishing features of the selected groups are the
level of education (school, university), writing style (official business, conversational, slang), and
vocabulary.
The results of the experiments are presented in Table. 2. Filtering unreliable data allowed to
improve the original accuracy of the models on average by 17.5%. The obtained accuracy values
allow to conclude that it is advisable to use CV-models to improve the training set and clean up
unreliable data.
Table 2
Results of experiments
Categories Accuracy for two categories Accuracy for three categories
Models Raw data Filtered data Raw data Filtered data
FastText 66,5% 82,1% 44,5% 62,4%
CRNN 63,8% 81,8% 43% 61,1%
In addition, the dataset was tested on another tool [44] designed to determine gender and age based
on quantitative parameters of texts, such as n-grams 3-8 characters. The result is 65% when
determining the age.
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6. Discussion
The fact that the wrong age is easily indicated in social networks is associated with an
improvement in the result, so the classification based on the original data shows the worst result. The
guarantee of correct age verification is to determine the age from the photo and compare it with the
age specified in the profile. There is a chance that the user will upload photos that are not their own,
but the result seems to be improving, which indicates the suitability of the described method.
7. References
The method described in the article is intended for determining the age of the author of a text
written in Russian. The approach includes FastText and VGG-Face models. The latter is used to filter
user photos.
During the study, it was found. that social media users intentionally indicate an age less than the
real one. In particular, this may be done for illegal actions-communication with young users. In turn,
the presented approach helps to counter such threats, and, as a result, pedophilia. The best result was
82.1% for the two categories. The result is obtained using the FastText model and filtered VGG-Face
data. The obtained accuracy of the model is comparable to the approaches of foreign researchers and
is sufficient for use in solving real problems.
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