Intelligence analysis is dependent on veracity assessment of Open Source Information (OSINF) which includes assessment of the reliability of sources and credibility of information. Traditionally, OSINF veracity assessment is done by intelligence analysts manually, but the large volumes, high velocity, and variety make it infeasible to continue doing so, and calls for automation. Based on meetings, interviews and questionnaires with military personnel, analysis of related work and state of the art, we identify the challenges and propose an approach and a corresponding framework for automated veracity assessment of OSINF. The framework provides a basis for new tools which will give the intelligence analysts the ability to automatically or semi-automatically assess veracity of larger amounts of data in a shorter amount of time. Instead of spending their time working with irrelevant, ambiguous, contradicting, biased, or plain wrong data, they can spend more time on analysis.

Fake news, malicious rumors, fabricated reviews, generated images and videos, are today spread at an unprecedented rate, making the task of manually assessing data veracity for decision-making purposes a daunting task. Hence, it is urgent to explore possibilities to perform automatic veracity assessment. In this work we review the literature in search for methods and techniques representing state of the art with regard to computerized veracity assessment. We study what others have done within the area of veracity assessment, especially targeted towards social media and open source data, to understand research trends and determine needs for future research. The most common veracity assessment method among the studied set of papers is to perform text analysis using supervised learning. Regarding methods for machine learning much has happened in the last couple of years related to the advancements made in deep learning. However, very few papers make use of these advancements. Also, the papers in general tend to have a narrow scope, as they focus on solving a small task with only one type of data from one main source. The overall veracity assessment problem is complex, requiring a combination of data sources, data types, indicators, and methods. Only a few papers take on such a broad scope, thus, demonstrating the relative immaturity of the veracity assessment domain.

Tracking how discussion topics evolve in social media and where these topics are discussed geographically over time has the potential to provide useful information for many different purposes. In crisis management, knowing a specific topic’s current geographical location could provide vital information to where, or even which, resources should be allocated. This paper describes an attempt to track online discussions geographically over time. A distributed geo-aware streaming latent Dirichlet allocation model was developed for the purpose of recognizing topics’ locations in unstructured text. To evaluate the model it has been implemented and used for automatic discovery and geographical tracking of election topics during parts of the 2016 American presidential primary elections. It was shown that the locations correlated with the actual election locations, and that the model provides a better geolocation classification compared to using a keyword-based approach.

For the competition SemEval-2017 we investigated the possibility of performing stance classification (support, deny, query or comment) for messages in Twitter conversation threads related to rumours. Stance classification is interesting since it can provide a basis for rumour veracity assessment. Our ensemble classification approach of combining convolutional neural networks with both automatic rule mining and manually written rules achieved a final accuracy of 74.9% on the competition's test data set for Task 8A. To improve classification we also experimented with data relabeling and using the grammatical structure of the tweet contents for classification.

The temptation to influence and sway public opinion most certainly increases with the growth of open online forums where anyone anonymously can express their views and opinions. Since online review sites are a popular venue for opinion influencing attacks, there is a need to automatically identify deceptive posts. The main focus of this work is on automatic identification of deceptive reviews, both positive and negative biased. With this objective, we build a deceptive review SVM based classification model and explore the performance impact of using different feature types (TF-IDF, word2vec, PCFG). Moreover, we study the transferability of trained classification models applied to review data sets of other types of products, and, the classifier robustness, i.e., the accuracy impact, against attacks by stylometry obfuscation trough machine translation. Our findings show that i) we achieve an accuracy of over 90% using different feature types, ii) the trained classification models do not perform well when applied on other data sets containing reviews of different products, and iii) machine translation only slightly impacts the results and can not be used as a viable attack method.