Dr. Ruben Sánchez Corcuera

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[u' @article{sanchez-corcuera_early_2024, title = {Early {Detection} and {Prevention} of {Malicious} {User} {Behavior} on {Twitter} {Using} {Deep} {Learning} {Techniques}}, issn = {2329-924X}, url = {https://ieeexplore.ieee.org/document/10597373/authors#authors}, doi = {10.1109/TCSS.2024.3419171}, abstract = {Organized misinformation campaigns on Twitter continue to proliferate, even as the platform acknowledges such activities through its transparency center. These deceptive initiatives significantly impact vital societal issues, including climate change, thus spurring research aimed at pinpointing and intercepting these malicious actors. Present-day algorithms for detecting bots harness an array of data drawn from user profiles, tweets, and network configurations, delivering commendable outcomes. Yet, these strategies mainly concentrate on postincident identification of malevolent users, hinging on static training datasets that categorize individuals based on historical activities. Diverging from this approach, we advocate for a forward-thinking methodology, which utilizes user data to foresee and mitigate potential threats before their realization, thereby cultivating more secure, equitable, and unbiased online communities. To this end, our proposed technique forecasts malevolent activities by tracing the projected trajectories of user embeddings before any malevolent action materializes. For validation, we employed a dynamic directed multigraph paradigm to chronicle the evolving engagements between Twitter users. When juxtaposed against the identical dataset, our technique eclipses contemporary methodologies by an impressive 40.66\\% in F score (F1 score) in the anticipatory identification of harmful users. Furthermore, we undertook a model evaluation exercise to gauge the efficiency of distinct system elements.}, urldate = {2024-09-04}, journal = {IEEE Transactions on Computational Social Systems}, author = {S\xe1nchez-Corcuera, Rub\xe9n and Zubiaga, Arkaitz and Almeida, Aitor}, year = {2024}, keywords = {Blogs, Chatbots, Climate change, Crowdsourcing, Data integrity, Detection algorithms, Fake news, Foreseeing, IF4.5, Information integrity, Malware, Q1, Social factors, Social networking (online), Twitter, malicious users, social networks}, pages = {1--13}, } ']

[u' @article{zulaika_zurimendi_lwp-wl_2022, title = {{LWP}-{WL}: {Link} weight prediction based on {CNNs} and the {Weisfeiler}-{Lehman} algorithm}, issn = {1568-4946}, shorttitle = {{LWP}-{WL}}, url = {https://www.sciencedirect.com/science/article/pii/S156849462200134X}, doi = {10.1016/j.asoc.2022.108657}, abstract = {We present a new technique for link weight prediction, the Link Weight Prediction Weisfeiler-Lehman (LWP-WL) method that learns from graph structure features and link relationship patterns. Inspired by the Weisfeiler-Lehman Neural Machine, LWP-WL extracts an enclosing subgraph for the target link and applies a graph labelling algorithm for weighted graphs to provide an ordered subgraph adjacency matrix into a neural network. The neural network contains a Convolutional Neural Network in the first layer that applies special filters adapted to the input graph representation. An extensive evaluation is provided that demonstrates an improvement over the state-of-the-art methods in several weighted graphs. Furthermore, we conduct an ablation study to show how adding different features to our approach improves our technique\u2019s performance. Finally, we also perform a study on the complexity and scalability of our algorithm. Unlike other approaches, LWP-WL does not rely on a specific graph heuristic and can perform well in different kinds of graphs.}, language = {en}, urldate = {2022-03-08}, journal = {Applied Soft Computing}, author = {Zulaika Zurimendi, Unai and S\xe1nchez-Corcuera, Rub\xe9n and Almeida, Aitor and L\xf3pez-de-Ipi\xf1a, Diego}, month = feb, year = {2022}, keywords = {FuturAAL, Graph mining, JCR6.725, Link weight prediction, Q1, SentientThings, Weisfeiler-Lehman algorithm, artificial intelligence, graph analysis, graph convolutional networks, link prediction, machine learning}, pages = {108657}, } ']

[u' @inproceedings{corcuera_achieving_2022, address = {Split}, title = {Achieving {Participatory} {Smart} {Cities} by {Making} {Social} {Networks} {Safer}}, isbn = {978-953-290-115-3}, abstract = {Cases of organised disinformation campaigns on Twitter, including those reported by the social network itself in its Transparency centre, continue unabated. The negative consequences of these attacks in processes of great importance to societies, such as electoral processes or vaccination campaigns, have sparked research into detecting this type of malicious user. State-of-the-art models for bot detection use numerous information collected from profiles, tweets, or network architecture to obtain competitive outcomes. On the other hand, these models allow for post-hoc detection of such users because they rely on fixed training datasets to classify users based on their previous activities. In contrast, we propose a proactive technique that uses user records to predict dangerous attacks before they occur as a measure to make social networks safer, fairer and less biased. For this purpose, our method uses a model that predicts malicious assaults by projecting users\u2019 embedding trajectories before completing their actions. We employed a Dynamic Directed Multigraph representation of temporal interactions between people in the Twittersphere for the experiments. By comparing them in the same data, our model outperforms state-of-the-art methods by 40.66\\% in F-score detecting malicious users preemptively. In addition, we propose a model selection study that evaluates the usefulness of several system components.}, language = {en}, booktitle = {Proceedings of the 7th {International} {Conference} on {Smart} and {Sustainable} {Technologies} ({Splitech} 2022)}, publisher = {FESB, University of Split}, author = {Corcuera, Ruben Sanchez and Zubiaga, Arkaitz and Almeida, Aitor}, month = apr, year = {2022}, keywords = {Foreseeing, Twitter, inception, machine learning, malicious users, social bots, social network analysis, social networks}, pages = {6}, } ']

[u' @article{sanchez-corcuera_analyzing_2021, title = {Analyzing the {Existence} of {Organization} {Specific} {Languages} on {Twitter}}, volume = {9}, issn = {2169-3536}, url = {https://ieeexplore.ieee.org/document/9507509/}, doi = {10.1109/ACCESS.2021.3102865}, abstract = {The presence of organisations in Online Social Networks (OSNs) has motivated malicious users to look for attack vectors, which are then used to increase the possibility of carrying out successful attacks and obtaining either private information or access to the organisation. This article hypothesised that organisations have speci\ufb01c languages that their members use in OSNs, which malicious users could potentially use to carry out an impersonation attack. To prove these speci\ufb01c languages, we propose two tasks: classifying tweets in isolation by their author\u2019s organisation and classifying users\u2019 entire timelines by organisation. To accomplish both tasks, we generate a dataset of over 15 million tweets of \ufb01ve organisations, and we apply language dependant models to test our hypothesis. Our results and the ablation study conclude that it is possible to classify tweets and users by organisation with more than three times the performance achieved by a traditional ML algorithm, showing a substantial potential for predicting the linguistic style of tweets.}, language = {en}, urldate = {2021-09-01}, journal = {IEEE Access}, author = {Sanchez-Corcuera, Ruben and Zubiaga, Arkaitz and Almeida, Aitor}, year = {2021}, keywords = {Adversarial information retrieval, Artificial Intelligence, IF3.367, Information inference, Natural language processing, Online social networks, Q2, Social networks, machine learning, nlp, social network analysis}, pages = {111463--111471}, } ']

[u' @article{sanchez-corcuera_analysing_2021, title = {Analysing centralities for organisational role inference in online social networks}, volume = {99}, issn = {09521976}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0952197620303663}, doi = {10.1016/j.engappai.2020.104129}, abstract = {The intensive use of Online Social Networks (OSN) nowadays has made users expose more information without realising it. Malicious users or marketing agencies are now able to infer information that is not published on OSNs by using data from targets friends to use for their benefit. In this paper, the authors present a generalisable method capable of deducing the roles of employees of an organisation using their Twitter relationships and the features of the graph from their organisation. The authors also conduct an extensive analysis of the node centralities to study their roles in the inference of the different classes proposed. Derived from the experiments and the ablation study conducted to the centralities, the authors conclude that the latent features of the graph along with the directed relationships perform better than previously proposed methods when classifying the role of the employees of an organisation. Additionally, to evaluate the method, the authors also contribute with a new dataset consisting of three directed graphs (one for each organisation) representing the relationships between the employees obtained from Twitter.}, language = {en}, urldate = {2021-01-04}, journal = {Engineering Applications of Artificial Intelligence}, author = {S\xe1nchez-Corcuera, Rub\xe9n and Bilbao-Jayo, Aritz and Zulaika, Unai and Almeida, Aitor}, month = mar, year = {2021}, keywords = {Adversarial information retrieval, Artificial Intelligence, Graph centralities, IF4.201, Information inference, Online social networks, Q1, machine learning, social network analysis, social networks}, pages = {104129}, } ']

[u' @article{sanchez-corcuera_persuasion-based_2020, title = {Persuasion-based recommender system ensambling matrix factorisation and active learning models}, issn = {1617-4909, 1617-4917}, url = {http://link.springer.com/10.1007/s00779-020-01382-7}, doi = {10.1007/s00779-020-01382-7}, abstract = {Recommendation systems are gaining popularity on Internet platforms such as Amazon, Netflix, Spotify or Booking. As more users are joining these online consumer and entertainment sectors, the profile-based data for providing accurate just-intime recommendations is rising thanks to strategies based on collaborative filtering or content-based metrics. However, these systems merely focus on providing the right item for the users without taking into account what would be the best strategy to suggest the movie, the product or the song (i.e. the strategy to increase the success or impact of the recommendation). Taking this research gap into consideration, this paper proposes a profile-based recommendation system that outputs a set of potential persuasive strategies that can be used with users with similar characteristics. The case study presented provides tailored persuasive strategies to make office-based employees enhance the energy efficiency at work (the dataset used on this research is specific of this sector). Throughout the paper, shreds of evidence are reported assessing the validity of the proposed system. Specifically, two approaches are compared: a profile-based recommendation system (RS) vs. the same RS enriched by adding an ensemble with an active learning model. The results shed light on not only providing effective mechanisms to increase the success of the recommendations but also alleviating the cold start problem when newcomers arrive.}, language = {en}, urldate = {2020-03-24}, journal = {Personal and Ubiquitous Computing}, author = {S\xe1nchez-Corcuera, Rub\xe9n and Casado-Mansilla, Diego and Borges, Cruz E. and L\xf3pez-de-Ipi\xf1a, Diego}, month = mar, year = {2020}, keywords = {Artificial Intelligence, IF1.735, Persuasive Technology, Q3, Recsys, cold-user problem, preference recommendation, sentientthings, user profiling}, } ']

[u' @inproceedings{sanchez-corcuera_persuade_2019, address = {Leicester, United Kingdom}, title = {Persuade {Me}!: {A} {User}-{Based} {Recommendation} {System} {Approach}}, isbn = {978-1-72814-034-6}, shorttitle = {Persuade {Me}!}, url = {https://ieeexplore.ieee.org/document/9060163/}, doi = {10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00310}, abstract = {Recommendation systems are gaining their momentum with popular Internet platforms such as Amazon, Net\ufb02ix or Spotify. As more users are joining these online consumer and entertainment sectors, the pro\ufb01le-based data for providing accurate just-in-time recommendations is rising thanks to strategies based on collaborative \ufb01ltering or content-based metrics. However, these systems merely focus on providing the right item for the users without taking into account what would be the best strategy to suggest the movie, the product or the song (i.e. the strategy to increase the success or impact of the recommendation). Taking this research gap into consideration, this paper proposes a pro\ufb01le-based recommendation system that outputs a set of potential persuasive strategies that can be used with users with similar characteristics. The scope of the tailored persuasive strategies is to make of\ufb01ce-based employees of tertiary buildings increase their pro-environmental awareness and enhance the energy ef\ufb01ciency at work (the dataset used on this research is speci\ufb01c of this sector). Throughout the paper, shreds of evidence are reported assessing the validity of the proposed system by not only providing effective mechanisms to increase the success of the recommendations but also alleviating the cold-start-problem when newcomers arrive.}, language = {en}, urldate = {2022-05-03}, booktitle = {2019 {IEEE} {SmartWorld}, {Ubiquitous} {Intelligence} \\& {Computing}, {Advanced} \\& {Trusted} {Computing}, {Scalable} {Computing} \\& {Communications}, {Cloud} \\& {Big} {Data} {Computing}, {Internet} of {People} and {Smart} {City} {Innovation} ({SmartWorld}/{SCALCOM}/{UIC}/{ATC}/{CBDCom}/{IOP}/{SCI})}, publisher = {IEEE}, author = {Sanchez-Corcuera, Ruben and Casado-Mansilla, Diego and Borges, Cruz E. and Lopez-De-Ipina, Diego}, month = aug, year = {2019}, keywords = {Feature extraction, Human factors, cold-start problem, collaborative filtering, energy conservation, persuasive strategies, recommendation systems, user profiling, workplace}, pages = {1740--1745}, } ']

[u' @article{sanchez-corcuera_smart_2019, title = {Smart cities survey: {Technologies}, application domains and challenges for the cities of the future}, volume = {15}, issn = {1550-1477}, shorttitle = {Smart cities survey}, url = {https://doi.org/10.1177/1550147719853984}, doi = {10.1177/1550147719853984}, abstract = {The introduction of the Information and Communication Technologies throughout the last decades has created a trend of providing daily objects with smartness, aiming to make human life more comfortable. The paradigm of Smart Cities arises as a response to the goal of creating the city of the future, where (1) the well-being and rights of their citizens are guaranteed, (2) industry and (3) urban planning is assessed from an environmental and sustainable viewpoint. Smart Cities still face some challenges in their implementation, but gradually more research projects of Smart Cities are funded and executed. Moreover, cities from all around the globe are implementing Smart City features to improve services or the quality of life of their citizens. Through this article, (1) we go through various definitions of Smart Cities in the literature, (2) we review the technologies and methodologies used nowadays, (3) we summarise the different domains of applications where these technologies and methodologies are applied (e.g. health and education), (4) we show the cities that have integrated the Smart City paradigm in their daily functioning and (5) we provide a review of the open research challenges. Finally, we discuss about the future opportunities for Smart Cities and the issues that must be tackled in order to move towards the cities of the future.}, language = {en}, number = {6}, urldate = {2019-06-10}, journal = {International Journal of Distributed Sensor Networks}, author = {S\xe1nchez-Corcuera, Ruben and Nu\xf1ez-Marcos, Adri\xe1n and Sesma-Solance, Jesus and Bilbao-Jayo, Aritz and Mulero, Rub\xe9n and Zulaika, Unai and Azkune, Gorka and Almeida, Aitor}, month = jun, year = {2019}, keywords = {Artificial Intelligence, IF1.151, IoT, Q4, Survey, architecture, co-creation, e-government, futuraal, smart cities}, pages = {1550147719853984}, } ']

[u' @inproceedings{papageorgiou_socio-economic_2019, address = {Leicester, United Kingdom}, title = {A {Socio}-{Economic} {Survey} for {Understanding} {Self}-{Perceived} {Effectiveness} of {Persuasive} {Strategies} {Towards} {Energy} {Efficiency} in {Tertiary} {Buildings}}, doi = {10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00321}, language = {English}, booktitle = {{IEEE} {SmartWorld}, {Ubiquitous} {Intelligence} \\& {Computing}, {Advanced} \\& {Trusted} {Computing}, {Scalable} {Computing} \\& {Communications}, {Cloud} \\& {Big} {Data} {Computing}, {Internet} of {People} and {Smart} {City} {Innovation} ({SmartWorld}/{SCALCOM}/{UIC}/{ATC}/{CBDCom}/{IOP}/{SCI})}, author = {Papageorgiou, Dimitris and Casado-Mansilla, Diego and Tsolakis, Apostolos and Borges, Cruz E. and L\xf3pez-De-Ipi\xf1a, Diego and Kamara-Esteban, Oihane and S\xe1nchez-Corcuera, Ruben and Moschos, Ioannis and Irizar-Arrieta, Ane and Krinidis, Stelios and Zacharaki, Angeliki and \xc1vila, Jose Manuel and Tzovaras, Dimitrios}, month = aug, year = {2019}, pages = {1817--1824} }']