=Paper=
{{Paper
|id=Vol-2606/7paper
|storemode=property
|title=Aggression Identification in Posts - two machine learning approache
|pdfUrl=https://ceur-ws.org/Vol-2606/7paper.pdf
|volume=Vol-2606
|authors=Faneva Ramiandrisoa
|dblpUrl=https://dblp.org/rec/conf/twsdetection/Ramiandrisoa20
}}
==Aggression Identification in Posts - two machine learning approache==
https://ceur-ws.org/Vol-2606/7paper.pdf
Aggression Identification in Posts - two machine
learning approaches.
Faneva Ramiandrisoa1,2[0000−0001−9386−3531]
1 IRIT & Université de Toulouse, France
faneva.ramiandrisoa@irit.fr
2 Université d’Antananarivo
Abstract. Social media have changed the way people communicate. One of the
aspects is cyber-aggression and interpersonal aggression that can be catalyzed by
perceived anonymity. Automatically monitoring user-generated content in order
to help moderating it is thus a hot topic. In this paper, we present and evaluate
two supervised machine learning models to identify aggressive content and the
level of aggressiveness. The first model uses random forest and linear regression
while the second model uses deep learning techniques.
Keywords: Social media; Social media analysis; Cyber-agression; TRAC Trolling,
Aggression and Cyberbulling; Machine learning based model
1 Introduction
Social media have changed the way people communicate [3,13,14,5]. One of these as-
pects is cyber-aggression and interpersonal aggression that can be catalyzed by per-
ceived anonymity [16]. Automatically monitoring user-generated content in order to
help moderating social media is thus an important although difficult topic [4,17].
In 2018, the Shared Task on Aggression Identification was organised as part of
the First Workshop on Trolling, Aggression and Cyberbullying (TRAC - 1) at COL-
ING 2018 [9]. The objective of this task is to detect aggressive content and the level
of aggressiveness. Thirty teams submitted their test runs. The best system obtained a
weighted F-score of 0.64 on a data set composed of annotated Facebook comments.
In this paper, we report two models we developed in order to answer the aggression
identification task. The first model uses random forest and linear regression which can
be considered as relatively mature approaches while the second model combines CNN
and LSTM recent deep learning techniques. No strong conclusion could be made on the
superiority of one or the other model since it depends on the collection.
This paper is organized as follows: Section 2 reports related works, Section 3 de-
scribes our two approaches, Section 4 describes the dataset used in this work, reports
the results and discuss them while Section 5 concludes this paper and presents future
works.
”Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).”
2 Faneva Ramiandrisoa
2 Related work
Approaches based on features and supervised classifiers such as Support Vector Ma-
chines (SVM) are often used in order to learn to detect whether a text contains ag-
gressiveness [24]; in recent years, deep learning has been also employed for this task
[19,2].
Deep learning has also been used by TRAC challenge participants. TRAC [9] chal-
lenge is the first that focuses on detecting aggressive text. The task training set is com-
posed of Facebook posts/comments; there is also two kinds of test sets: one from Face-
book and another from Twitter.
Among the thirty participants, Saroyehun [2] obtained the best results. The authors
investigated the efficacy of deep neural network by experimenting different models
: CNN, LSTM, BiLSTM, and combinations thereof. In their experiments they used
translation technique to enlarge the training set and added an external dataset on hate
speech3 . The LSTM model which was trained on the augmented training set only,
achieved the best weighted F1 score of 0.6425 on Facebook test set ; it is the first
ranked system on TRAC challenge ; the same system does not performed as well on the
Twitter data set. The other system of the same team which implements a combination
of CNN and LSTM and which was trained on the augmented training set and the addi-
tional dataset, achieved a weighted F1 score of 0.5920 and the third rank on the twitter
test set.
Raiyani et. al. [20], meanwhile, tested different models for text classification in
TRAC, from classic machine learning model to deep learning models. At the end, they
kept three models: FastText model, Dense neural networks, and Voting of the two. The
Dense neural networks gives better performance than the two others and achieved a
weighted F1 score of 0.5813 on Facebook test set; it is the fourteenth rank on TRAC
challenge. While it achieved the best weighted F1 score of 0.6009 and the first rank on
the twitter test set, although it was trained on a Facebook dataset.
3 Machine learning based models
We developed two supervised machine learning based models that we evaluated in this
paper. The first method combines random forest and logistic regression while the second
approach is deep learning based. We also developed a model based on CNN only for
which results can be found in [21]; it performs in between the two models reported in
this paper.
3.1 Trac-RF LR: combination of two classifiers
In this model we combined random forest (RF) based on surface features and linguistic
features with logistic regression (LR) based on document vectorization. We chose this
combination because a combination of multiple machine learning models placed first
in many prestigious machine learning competitions [18], such as Netflix Competition,
3 https://github.com/ZeerakW/hatespeech, accessed on January 10, 2020
Aggression Identification in Posts - two machine learning approaches. 3
Kaggle,... Moreover, when using non-combined models on the training dataset, the re-
sults were lower in the case of TRAC as well and this was confirmed on the test set (see
section 4.3).
RF Classifier. The random forest model uses different features extracted from the
comments as presented in Table 1. Some are adapted from [1,22] where the authors tried
to detect depression from texts; another source of inspiration is [7] where the authors
suggested an information nutritional label for describing text qualities.
Name Hypothesis or tool/resource used
Part-of-speech fre- Normalized frequencies of each tag: adjectives, verbs, nouns
quency and adverbs (four features).
Negation Normalized frequencies of negative words like: no, not, didn’t,
can’t, ... The idea behind is to detect non direct aggressiveness.
Capitalized The idea behind is that aggressive texts tend to put emphasis
on the target they mention. It can indicate feelings or speaking
volume.
Punctuation marks ! or ? or any combination of both can emphasize offensiveness
of texts.
Emoticons Another way to express sentiment or feeling.
Sentiment Use of NRC-Sentiment-Emotion-Lexicons4 to trace the polarity
in text.
Emotions Frequency of emotions from specific categories: anger, fear, sur-
prise, sadness and disgust. The idea behind is to check the cate-
gories related to aggressiveness.
Gunning Fog Index Estimate of the years of education that a person needs to under-
stand the text at first reading.
Flesch Reading Ease Measure how difficult to understand a text is.
Linsear Write Formula Developed for the U.S. Air Force to calculate the readability of
their technical manuals5 .
New Dale-Chall Read- Measure the difficulty of comprehension that persons encounter
ability when reading a text. It is inspired from Flesch Reading Ease
measure.
Swear words The intuition behind is that the texts containing insults are often
aggressive.
Lexical analysis with Empath is a tool for analyzing text across lexical categories.
python library empath By default, it has 194 lexical categories and each category is
considered as feature.
Table 1: List of features used in RF to represent texts (Facebook comments or tweets).
4 http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm, accessed on 2017-
02-23
4 Faneva Ramiandrisoa
Some of these features are used for abusive language detection, hate speech, cyber-
bullying and the others are used for sentiment or personality analysis that we judged
useful for aggression detection.
A RF classifier was trained on train and validation sets by representing each text
(Facebook comment or tweet) with a vector composed by the features we mentioned in
Table 1.
The following parameters were used during the training: class weight=”balanced”,
max features=”sqrt”, n estimators=60, min weight fraction leaf=0.0, criterion=’entropy’,
random state=2.
At prediction time, a text from the test set is represented with features and then run
the trained model. The output is the estimated probabilities for the three classes (overtly
aggressive, covertly aggressive and non-aggressive).
LR Classifier. This model is based on document vectorization using Doc2vec [12].
Doc2vec is used to represent sentences, paragraphs, or whole documents as vectors and
it can be trained on small corpora, which is case of the task datasets.
Before building the LR Classifier, we first trained two separate Doc2vec models: a
Distributed Bag of Words and a Distributed Memory model [12]. For the training, we
used the same configuration as in [25] for representing user’s text. The two Doc2vec
models were trained on the train and validation sets. We used the Python package gen-
sim6 [23]. We also concatenated the output vectors of these two models, as done in [25],
resulting in a representation by a 200-dimension vector per text.
Then a logistic regression classifier was trained on the vectors for both the train and
validation sets with the following parameters : class weight=”balanced”, random state=1,
max iter=100, solver=”liblinear”.
At prediction time, the texts from the test set were vectorized by using the two
Doc2vec models and the 200-dimension vectors were given as input of trained classifier.
The output is also a set of class probabilities.
Combination of two classifiers. The class probabilities obtained from RF classifier
and LR Classifier were averaged and finally the class with the highest probability was
considered as the class the text belongs to. We also tested different ways to combine the
output probabilities obtained from the two classifiers RF and LR, such as maximum,
minimum, etc., but the average method gave the best results.
3.2 Trac-CNN LSTM: Combination of CNN and LSTM
This model combines two deep learning techniques: CNN and LSTM. The main idea
is to pass input representation (sentence matrix in Figure 1) to the CNN and pass the
local features learnt by the CNN (concatenated vectors in Figure 1) to the LSTM. In-
deed, CNN and LSTM are complementary due to the fact that each of them captures
information at different scales [2].
5 http://www.streetdirectory.com/travel_guide/15675/writing/how_to_choose_
the_best_readability_formula_for_your_document.html, accessed on 2018-02-25
6 https://radimrehurek.com/gensim/index.html
Aggression Identification in Posts - two machine learning approaches. 5
The architecture of our combined model is illustrated in Figure 1. It is as follows:
first, we convert sentences/texts into sentences matrix7 where each row is a vector rep-
resentation8 of each word in the sentences/texts. Then, convolutions are applied on the
sentences matrix where we used three filter region sizes: bigrams (height = 2), trigrams
(height = 3) and fourgrams (height = 4). Each region has 100 filters; thus, in total there
are 300 filters. The result of convolutions is called feature maps; vectors with variable-
length according to the region filter and each filter region has 100 feature maps. Af-
terwards, a 1-max pooling is performed over feature maps. More precisely, for each
region the largest number from each feature map is kept and then concatenated to form
a vector. As a result, we obtain one vector of size 1009 per region filter. Then, these three
vectors are concatenated to form a feature vector and a dropout is applied on this feature
vector. The concatenated feature vector is passed to the LSTM layer. Then, we added
one fully connected hidden layer to reduce the dimension of the concatenated vector,
followed by a dropout. Finally, an output layer, which is also a fully connected layer
with three possible output states, is added. On the output layer, the activation function
used is the softmax function.
The architecture of our model is inspired from the CNN architecture Zhang et.
al. [26] proposed and which is used for sentences classification. In that task, their CNN
architecture outperforms baseline methods which use SVM as well as the one that used
CNN in [8].
4 Evaluation
4.1 Data set
The evaluation is based on the TRAC 2018 shared task [9]. The task dataset is a subset
of Kumar et al’ [10] and consists in English and Hindi randomly sampled Facebook
comments. In this study, we focused on the English part of the dataset which is detailed
in Table 2. It is composed of (a) 11,999 Facebook comments for training and 3,001
comments for validation. It is annotated with 3 levels of aggression - Overtly Aggres-
sive (OAG), Covertly Aggressive (CAG) and Non- Aggressive (NAG), (b) 916 English
comments for test. Additionally, 1,257 English tweets were given as a second test set.
4.2 Evaluation measure
The evaluation metric used in this paper is the weighted F1 which was also used in the
TRAC shared task. The weighted F1 is equal to the average, weighted by the number
of instances for each label, of the F1 (given by equation 1) of each class label.
R∗P
F1 = 2 (1)
R+P
7 The dimension of a sentence matrix is l × d, where l is the length of the longest text/sentence
in the dataset and d is the dimension of word vector representation.
8 The word vector representation is obtained with word2vec model [15] trained on the training
and validation sets.
9 Because there is 100 feature maps.
6 Faneva Ramiandrisoa
Fig. 1: Illustration of a CNN + LSTM architecture for aggression detection inspired
from [26].
Test
Number of Train Validation
Facebook Twitter
texts (=posts+comments) 11,999 3,001 916 1,257
Overt aggression 2,708 711 144 361
Covert aggression 4,240 1,057 142 413
No aggression 5,051 1,233 630 483
Table 2: Distribution of training, validation and testing data on TRAC 2018 data collec-
tion.
tp tp
where P = t p+ f p is the precision, R = t p+ f n is the recall, t p denotes the true posi-
tives, f p the false positives, and f n the false negatives.
4.3 Results
Table 3 reports the results we obtained with the two models presented above. For com-
parison, we report also results obtained with the RF classifier only and with the LR
classifier only. The baseline mentioned in the first row was given by the TRAC shared
task organizers while the second row is the best result from participants in the TRAC
workshop.
Aggression Identification in Posts - two machine learning approaches. 7
Weighted F1
System
Facebook Twitter
Random Baseline 0.354 0.348
Saroyehun [2] 0.642 0.592
Trac-RF LR 0.581 0.409
Trac-CNN LSTM 0.559 0.511
Trac-RF only 0.573 0.397
Trac-LR only 0.569 0.452
Table 3: Results for the English (Facebook and Twitter) task. Bold value is the best
performance for our approaches.
We can see that our two models outperform the baseline on both Facebook and
Twitter subsets. Trac-RF-LR is better than Trac-CNN-LSTM on the Facebook collec-
tion while it is the opposite on the Twitter collection. This could be due to the train
dataset which is only composed of texts crawled from Facebook. Indeed, we can ob-
serve the same behaviour for the other systems that participated to the challenge [9].
The only exception is for Saroyehun [2] system which performs better on the Twitter
dataset.
5 Conclusion
In this paper, we presented two different supervised machine learning approaches for
aggression identification on TRAC 2018 English collections (Facebook and Twitter
based). The combination of random forest and linear regression classifiers based on
a set of surface features and document vectorization leaded to the sixteenth ranked
system out of thirty on the Facebook collection. The combination of CNN and Long
Short-Term Memory was ranked fifteenth out of thirty systems.
To extend this work, we plan to update our models by adding new features such as
bag of words or features more specific to the aggression. We also plan to apply feature
engineering on the features we used in this paper in order to see which one are the
most useful. On the other hand, feature selection could also be applied to build models
that use features as less as possible [11,6]. Finally, an investigation on deep learning
models will be conducted by using different architectures such as hierarchical attention
network. We do believe that these tracks can help designing more performing models.
Ethical issue. While TRAC challenge has its proper ethical policies, detecting ag-
gressive content from user’s posts raises ethical issues that are beyond the scope of the
paper.
Acknowledgement. This work has been partially funded by the European Union’s
Horizon 2020 H2020-SU-SEC-2018 under the Grant Agreement n°833115 (PREVI-
SION project). This work has also been partially supported by the Ministère des Af-
faires étrangères et du Développement international under the scholarship EIFFEL-
DOCTORAT 2017/ n°P707544H for Faneva Ramiandrisoa’s PhD thesis.
8 Faneva Ramiandrisoa
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