The amount of data generated by the current interconnected world is immeasurable, and a large part of such data is publicly available, which means that it is accessible by any user, at any time, from anywhere in the Internet. In this respect, Open Source Intelligence (OSINT) is a type of intelligence that actually benefits from that open natureby collecting, processing and correlating points of the whole cyberspace to generate knowledge. In fact, recent advances in technology are causing OSINT to currently evolve at a dizzying rate, providing innovative data-driven and AI-powered applications for politics, economy or society, but also offering new lines of action against cyberthreats and cybercrime. The paper at hand describes the current state of OSINT and makes a comprehensive review of the paradigm, focusing on the services and techniques enhancing the cybersecurity field. On the one hand, we analyze the strong points of this methodology and propose numerous ways to apply it to cybersecurity. On the other hand, we cover the limitations when adopting it. Considering there is a lot left to explore in this ample field, we also enumerate some open challenges to be addressed in the future. Additionally, we study the role of OSINT in the public sphere of governments, which constitute an ideal landscape to exploit open data.

While social media has been proved as an exceptionally useful tool to interact with other people and massively and quickly spread helpful information, its great potential has been ill-intentionally leveraged as well to distort political elections and manipulate constituents. In the paper at hand, we analyzed the presence and behavior of social bots on Twitter in the context of the November 2019 Spanish general election. Throughout our study, we classified involved users as social bots or humans, and examined their interactions from a quantitative (i.e., amount of traffic generated and existing relations) and qualitative (i.e., user's political affinity and sentiment towards the most important parties) perspectives. Results demonstrated that a non-negligible amount of those bots actively participated in the election, supporting each of the five principal political parties.

Since the first reported case in Wuhan in late 2019, COVID-19 has rapidly spread worldwide, dramatically impacting the lives of millions of citizens. To deal with the severe crisis resulting from the pandemic, worldwide institutions have been forced to make decisions that 1 affect the socio-economic realm. In this sense, researchers from diverse knowledge areas are investigating the behavior of the disease in a rush against time. In both cases, the lack of reliable data has been an obstacle to carry out such tasks with accuracy. To tackle this challenge, COnVIDa (https://convida.inf.um.es) has been designed and developed as a user-friendly tool that easily gathers rigorous multidisciplinary data related to the COVID-19 pandemic from different data sources. In particular, the pandemic expansion is analyzed with variables of health nature, but also social ones, mobility, etc. Besides, COnVIDa permits to smoothly join such data, compare and download them for further analysis. Due to the open-science nature of the project, COnVIDa is easily extensible to any other region of the planet. In this way, COnVIDa becomes a data facilitator for decision-making processes, as well as a catalyst for new scientific researches related to this pandemic.

Prevention of cybercrime is one of the missions of Law Enforcement Agencies (LEA) aiming to protect and guarantee sovereignty in the cyberspace. In this regard, online sex crimes are among the principal ones to prevent, especially those where a child is abused. The paper at hand proposes C3-Sex, a smart chatbot that uses Natural Language Processing (NLP) to interact with suspects in order to profile their interest regarding online child sexual abuse. This solution is based on our Artificial Conversational Entity (ACE) that connects to different online chat services to start a conversation. The ACE is designed using generative and rule-based models in charge of generating the posts and replies that constitute the conversation from the chatbot side. The proposed solution also includes a module to analyze the conversations performed by the chatbot and calculate a set of 25 features that describes the suspect’s behavior. After 50 days of experiments, the chatbot generated a dataset with 7199 profiling vectors with the features associated to each suspect. Afterward, we applied an unsupervised method to describe the results that differentiate three groups, which we categorize as indifferent, interested, and pervert. Exhaustive analysis is conducted to validate the applicability and advantages of our solution.

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The term social bots refer to software-controlled accounts that actively participate in the social platforms to influence public opinion toward desired directions. To this extent, this data descriptor presents a Twitter dataset collected from October 4th to November 11th, 2019, within the context of the Spanish general election. Starting from 46 hashtags, the collection contains almost eight hundred thousand users involved in political discussions, with a total of 5.8 million tweets. The proposed data descriptor is related to the research article available at [1] . Its main objectives are: i) to enable worldwide researchers to improve the data gathering, organization, and preprocessing phases; ii) to test machine-learning-powered proposals; and, finally, iii) to improve state-of-the-art solutions on social bots detection, analysis, and classification. Note that the data are anonymized to preserve the privacy of the users. Throughout our analysis, we enriched the collected data with meaningful features in addition to the ones provided by Twitter. In particular, the tweets collection presents the tweets’ topic mentions and keywords (in the form of political bag-of-words), and the sentiment score. The users’ collection includes one field indicating the likelihood of one account being a bot. Furthermore, for those accounts classified as bots, it also includes a score that indicates the affinity to a political party and the followers/followings list.

Social networks have triumphed in communicating people online, but they have also been exploited to launch influence operations for manipulating society. The deployment of software-controlled accounts (e.g., social bots) has proven to be one of the most effective enablers for that purpose, and tools for their detection have been developed and widely adopted. However, the way to analyze these accounts and measure their impact is heterogeneous in the literature, where each case study performs unique measurements. To unify these efforts, we propose a common framework to analyze the interference of social bots in Twitter. The methodology compares the nonauthentic actors with the rest of users from different perspectives, thus building objective metrics to measure their actual impact. We validate the framework by applying it to a dataset of Twitter iterations dated in the weeks preceding the 2019 Spanish general election. In this sense, we check that our framework facilitates the quantitative evaluation of unauthentic groups, particularly discovering that social bots changed the natural dynamics of the network in these days, but did not have a significant impact. We also consider this methodology as a practical tool for the qualitative interpretation of experimental results, particularly suggesting within the aforementioned electoral context that semiautomated accounts are potentially more threatening than fully automated ones.