The previous part discusses the GI/BSI/DFKI Protection Profile which constitutes after the implementation of the identified improvements as the proposed evaluation methodology for remote electronic voting systems. The result can now be applied to available systems. Currently, there is no system that has been evaluated against the GI/BSI/DFKI Protection Profile or even against the improved version.

Proof of Concept

Project scheduling under uncertainty survey and research potentials

The veil of anonymity provided by smartphones with pre-paid SIM cards, public Wi-Fi hotspots, and distributed networks like Tor has drastically complicated the task of identifying users of social media during forensic investigations. In some cases, the text of a single posted message will be the only clue to an author’s identity. How can we accurately predict who that author might be when the message may never exceed 140 characters on a service like Twitter? For the past 50 years, linguists, computer scientists, and scholars of the humanities have been jointly developing automated methods to identify authors based on the style of their writing. All authors possess peculiarities of habit that influence the form and content of their written works. These characteristics can often be quantified and measured using machine learning algorithms. In this paper, we provide a comprehensive review of the methods of authorship attribution that can be applied to the problem of social media forensics. Furthermore, we examine emerging supervised learning-based methods that are effective for small sample sizes, and provide step-by-step explanations for several scalable approaches as instructional case studies for newcomers to the field. We argue that there is a significant need in forensics for new authorship attribution algorithms that can exploit context, can process multi-modal data, and are tolerant to incomplete knowledge of the space of all possible authors at training time.

Authorship Attribution for Social Media Forensics

Fake consumer review detection has attracted much interest in recent years owing to the increasing number of Internet purchases. Existing approaches to detect fake consumer reviews use the review content, product and reviewer information and other features to detect fake reviews. However, as shown in recent studies, the semantic meaning of reviews might be particularly important for text classification. In addition, the emotions hidden in the reviews may represent another potential indicator of fake content. To improve the performance of fake review detection, here we propose two neural network models that integrate traditional bag-of-words as well as the word context and consumer emotions. Specifically, the models learn document-level representation by using three sets of features: (1) n-grams, (2) word embeddings and (3) various lexicon-based emotion indicators. Such a high-dimensional feature representation is used to classify fake reviews into four domains. To demonstrate the effectiveness of the presented detection systems, we compare their classification performance with several state-of-the-art methods for fake review detection. The proposed systems perform well on all datasets, irrespective of their sentiment polarity and product category.

Fake consumer review detection using deep neural networks integrating word embeddings and emotion mining

Aspect based sentiment analysis by a linguistically regularized CNN with gated mechanism

The rapid increase in data on social media creates a need for mining such data to get valuable insights. The data type can be unstructured with large volumes. Sentiment analysis addresses such need by detecting opinions or emotions on the social media text. Sentiment analysis can be performed in various domains such as social, medical and industrial applications. This paper presents a survey about sentiment analysis addressing the different concepts in this area, problems and its solutions, available APIs, tools used and presenting a list of open challenges in this area.

Sentiment Analysis over Social Networks: An Overview

Social networks have become a convenient and effective means of communication in recent years. Many people use social networks to communicate, lead, and manage activities, and express their opinions in supporting or opposing different causes. This has brought forward the issue of verifying the owners of social accounts, in order to eliminate the effect of any fake accounts on the people. This study aims to authenticate the genuine accounts versus fake account using writeprint, which is the writing style biometric. We first extract a set of features using text mining techniques. Then, gtraining of a supervised machine learning algorithm to build the knowledge base is conducted. The recognition procedure starts by extracting the relevant features and then measuring the similarity of the feature vector with respect to all feature vectors in the knowledge base. Then, the most similar vector is identified as the verified account.

Recognising User Identity in Twitter Social Networks via Text Mining

Event detection using social media streams needs a set of informative features with strong signals that need minimal preprocessing and are highly associated with events of interest. Identifying these informative features as keywords from Twitter is challenging, as people use informal language to express their thoughts and feelings. This informality includes acronyms, misspelled words, synonyms, transliteration and ambiguous terms. In this paper, we propose an efficient method to select the keywords frequently used in Twitter that are mostly associated with events of interest such as protests. The volume of these keywords is tracked in real time to identify the events of interest in a binary classification scheme. We use keywords within word-pairs to capture the context. The proposed method is to binarize vectors of daily counts for each word-pair by applying a spike detection temporal filter, then use the Jaccard metric to measure the similarity of the binary vector for each word-pair with the binary vector describing event occurrence. The top n word-pairs are used as features to classify any day to be an event or non-event day. The selected features are tested using multiple classifiers such as Naive Bayes, SVM, Logistic Regression, KNN and decision trees. They all produced AUC ROC scores up to 0.91 and F1 scores up to 0.79. The experiment is performed using the English language in multiple cities such as Melbourne, Sydney and Brisbane as well as the Indonesian language in Jakarta. The two experiments, comprising different languages and locations, yielded similar results.

Event Detection in Twitter: A Keyword Volume Approach

Selecting keywords from Twitter as features to identify events is challenging due to language informality such as acronyms, misspelled words, synonyms, transliteration and ambiguous terms. In this paper, We compare and identify the best methods for keyword selection as features to be used for classification purposes. Specifically, we study the aspects affecting keywords as features to identify civil unrest and protests. These aspects include the word count, the word forms such as n-gram, skip-gram and bags-of-words as well as the data association methods including correlation techniques and similarity techniques. To test the impact of the mentioned factors, we developed a framework that analyzed 641 days of tweets and extracted the words highly associated with event days along the same time frame. Then, we used the extracted words as features to classify any single day to be either an event day or a nonevent day in a specific location. In this framework, we used the same pipeline of data cleaning, prepossessing, feature selection, model learning and event classification using all combinations of keyword selection criteria. We used Naive Bayes classifier to learn the selected features and accordingly predict the event days. The classification is tested using multiple metrics, such as accuracy, precision, recall, F-score and AUC. This study concluded that the best word form is bag-of-words with average AUC of 0.72 and the best word count is two with average AUC of 0.74 and the best feature selection method is Spearman's correlation with average AUC of 0.89 and the best classifier for event detection is Naive Bayes Classifier.

Ahmad Hany Hossny

Papers

Sentiment Analysis over Social Networks: An Overview

Recognising User Identity in Twitter Social Networks via Text Mining

Event Detection in Twitter: A Keyword Volume Approach

Feature selection methods for event detection in Twitter: a text mining approach

Detecting explosives by PGNAA using KNN Regressors and decision tree classifier: A proof of concept