Detection of Bullying Messages in Social Media
As a side effect of increasingly popular social media, cyber bullying has emerged as a serious problem afflicting children,adolescents and young adults. Machine learning techniques make automatic detection of bullying messages in social media possible,and this could help to construct a healthy and safe social media environment. In this meaningful research area, one critical issue is robust and discriminative numerical representation learning of text messages. In this paper, we propose a new representation learning method to tackle this problem. Our method named Semantic-Enhanced Marginalized Denoising Auto-Encoder (smSDA) is developed via semantic extension of the popular deep learning model stacked denoising autoencoder. The semantic extension consists of semantic dropout noise and sparsity constraints, where the semantic dropout noise is designed based on domain knowledge and the word embedding technique. Our proposed method is able to exploit the hidden feature structure of bullying information and learn arobust and discriminative representation of text. Comprehensive experiments on two public cyber bullying corpora (Twitter andMySpace) are conducted, and the results show that our proposed approaches outperform other baseline text representation learning methods.
PROJECT OUTPUT VIDEO:
- Previous works on computational studies of bullying have shown that natural language processing and machine learning are powerful tools to study bullying.
- Cyber bullying detection can be formulated as a supervised learning problem. A classifier is first trained on a cyber bullying corpus labeled by humans, and the learned classifier is then used to recognize a bullying message.
- Yin et.al proposed to combine BoW features, sentiment features and contextual features to train a support vectormachine for online harassment detection.
- al utilized label specific features to extend the general features, where the label specific features are learned byLinear Discriminative Analysis. In addition, commonsense knowledge was also applied.
- Nahar et.al presented aweighted TF-IDF scheme via scaling bullying-like features by a factor of two. Besides content-based information,Maral et.al proposed to apply users’ information, such as gender and history messages, and context information as extra features
DISADVANTAGES OF EXISTING SYSTEM:
- The first and also critical step is the numerical representation learning for text messages.
- Secondly, cyber bullying is hard to describe and judge from a third view due to its intrinsic ambiguities.
- Thirdly, due to protection of Internet users and privacy issues, only a small portion of messages are left on the Internet, and most bullying posts are deleted.
- Three kinds of information including text, user demography, and social network features are often used in cyber bullying detection. Since the text content is the most reliable, our work here focuses on text-based cyber bullying detection.
- In this paper, we investigate one deep learning method named stacked denoising autoencoder (SDA). SDA stacks several denoising autoencoders and concatenates the output of each layer as the learned representation. Each denoising autoencoder in SDA is trained to recover the input data from a corrupted version of it. The input is corrupted by randomly setting some of the input to zero, which is called dropout noise. This denoising process helps the autoencoders to learn robust representation.
- In addition, each autoencoder layer is intended to learn an increasingly abstract representation of the input.
- In this paper, we develop a new text representation model based on a variant of SDA: marginalized stacked denoising autoencoders (mSDA), which adopts linear instead of nonlinear projection to accelerate training and marginalizes infinite noise distribution in order to learn more robust representations.
- We utilize semantic information to expand mSDA and develop Semantic-enhanced Marginalized Stacked Denoising Autoencoders (smSDA). The semantic information consists of bullying words. An automatic extraction of bullying words based on word embeddings is proposed so that the involved human labor can be reduced. During training of smSDA, we attempt to reconstruct bullying features from other normal words by discovering the latent structure, i.e. correlation, between bullying and normal words. The intuition behind this idea is that some bullying messages do not contain bullying words. The correlation information discovered by smSDA helps to reconstruct bullying features from normal words, and this in turn facilitates detection of bullying messages without containing bullying words.
ADVANTAGES OF PROPOSED SYSTEM:
- Our proposed Semantic-enhanced Marginalized Stacked Denoising Autoencoder is able to learn robust features from BoW representation in an efficientand effective way. These robust features are learned by reconstructing original input from corrupted(i.e., missing) ones. The new feature spacecan improve the performance of cyber bullying detection even with a small labeled training corpus.
- Semantic information is incorporated into the reconstruction process via the designing of semantic dropout noises and imposing sparsity constraints on mapping matrix. In our framework, high-quality semantic information, i.e., bullying words, can be extracted automatically through word embeddings.
- Finally, these specialized modifications make the new feature space more discriminative and this in turn facilitates bullying detection.
- Comprehensive experiments on real-data sets have verified the performance of our proposed model.
- OSN System Construction Module
- Construction of Bullying Feature Set
- Cyber bullying Detection.
- Semantic-Enhanced Marginalized Denoising Auto-Encoder.
OSN System Construction Module
- In the first module, we develop the Online Social Networking (OSN) system module. We build up the system with the feature of Online Social Networking. Where, this module is used for new user registrations and after registrations the users can login with their authentication.
- Where after the existing users can send messages to privately and publicly, options are built. Users can also share post with others. The user can able to search the other user profiles and public posts. In this module users can also accept and send friend requests.
- With all the basic feature of Online Social Networking System modules is build up in the initial module, to prove and evaluate our system features.
Construction of Bullying Feature Set:
- The bullying features play an importantrole and should be chosen properly. In the following, the steps for constructing bullying feature set Zb are given, inwhich the first layer and the other layers are addressedseparately.
- For the first layer, expert knowledge and word embeddings are used. For the other layers, discriminative feature selection is conducted.
- In this module firstly, we build a list of words with negative affective, including swear words and dirty words. Then, we compare the word list with the BoW features of our own corpus, and regard the intersections as bullying features.
- Finally, the constructed bullying features are used to train the first layer in our proposed sm SDA. It includes two parts: one is the original insulting seeds based on domain knowledge and the other is the extended bullying words via word embeddings
- Observe Attentively Over A Period Of Time.
- In this module we propose the Semantic-enhanced Marginalized Stacked Denoising Auto-encoder (smSDA). In this module, we describe how to leverage it for cyber bullying detection. smSDA provides robust and discriminative representations The learned numerical representations can then be fed into our system.
- In the new space, due to the captured feature correlation and semantic information, even trained in a small size of training corpus, is able to achieve a good performance on testing documents.
- Based on word embeddings, bullying features canbe extracted automatically. In addition, the possible limitation of expert knowledge can be alleviated bythe use of word embedding
- BLOCK THE ACCOUNTS:
- Abnormal user.
- Cyber- Crime user.
Semantic-Enhanced Marginalized Denoising Auto-Encoder:
- An automatic extraction of bullying words based on word embeddings is proposed so that the involved human labor can be reduced. During training of smSDA, we attempt to reconstruct bullying features from other normal words by discovering the latent structure, i.e. correlation, between bullying and normal words. The intuition behind this idea is that some bullying messages do not contain bullying words.
- The correlation information discovered by smSDA helps to reconstruct bullying features from normal words, and this in turn facilitates detection of bullying messages without containing bullying words. For example, there is a strong correlation between bullying word fuck and normal word off since they often occur together.
- If bullying messages do not contain such obvious bullying features, such as fuck is often misspelled as fck, the correlation may help to reconstruct the bullying features from normal ones so that the bullying message can be detected. It should be noted that introducing dropout noise has the effects of enlarging the size of the dataset, including training data size, which helps alleviate the data sparsity problem.
- System : Pentium Dual Core.
- Hard Disk : 120 GB.
- Monitor : 15’’LED
- Input Devices : Keyboard, Mouse
- Ram : 1 GB
- Operating system : Windows 7.
- Coding Language : JAVA/J2EE
- Tool : Netbeans 7.2.1
- Database : MYSQL