Assessing the Effectiveness of a Gamified Social Network for Applying Privacy Concepts: An Empirical Study with Teens

[EN] The concept of privacy in online social networks (OSNs) is a challenge, especially for teenagers. Previous works deal with teaching about privacy using educational online content, and media literacy. However, these tools do not necessarily promote less risky behaviors, and do not allow the assessment of users' behavior after the learning period. Moreover, few research studies about the effects of social gamification have been performed for this population segment (i.e., teenagers). To address this problem in this article, we propose the use of gamification in an OSN called Pesedia to facilitate the teaching/learning process, and assess its effectiveness in promoting suitable privacy behaviors. We tested our proposal comparing teenagers' performance in two editions of a course about social networks, and privacy (with, and without gamification) for one month. We measured the impact of gamification in the participants' behaviors toward privacy concepts as a consequence of the privacy teaching/learning process, and the participants' engagement in the educational process. The results show that there are significant differences in participants' behavior regarding privacy, and engagement in the gamified social network. Moreover, there is also a significant difference in participants' engagement for the gamified male participants. The gamified social network proposed in this article may be relevant, and useful for educators who wish to develop, and enhance teenagers' privacy skills, or for a broader base of aspects related to the development of digital competences, and technology in education.This work was supported in part by the Spanish Government Project TIN2017-89156-R, and in part by the FPI under Grant BFS-2015-074498. (Corresponding author: Elena Del Vol.)Alemany-Bordera, J.; Del Val, E.; García-Fornes, A. (2020). Assessing the Effectiveness of a Gamified Social Network for Applying Privacy Concepts: An Empirical Study with Teens. IEEE Transactions on Learning Technologies. 13(4):777-789. https://doi.org/10.1109/TLT.2020.3026584S77778913

Multi-dimensional adaptation in MAS organizations

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Organization adaptation requires determining the consequences of applying changes not only in terms of the benefits provided but also measuring the adaptation costs as well as the impact that these changes have on all of the components of the organization. In this paper, we provide an approach for adaptation in multiagent systems based on a multidimensional transition deliberation mechanism (MTDM). This approach considers transitions in multiple dimensions and is aimed at obtaining the adaptation with the highest potential for improvement in utility based on the costs of adaptation. The approach provides an accurate measurement of the impact of the adaptation since it determines the organization that is to be transitioned to as well as the changes required to carry out this transition. We show an example of adaptation in a service provider network environment in order to demonstrate that the measurement of the adaptation consequences taken by the MTDM improves the organization performance more than the other approaches.Manuscript received January 2, 2012; revised July 26, 2012; accepted August 7, 2012. Date of publication August 31, 2012; date of current version April 16, 2013. This work was supported in part by projects TIN2008-04446 and TIN2009-13839-C03-01. J. M. Alberola received a Grant from Ministerio de Ciencia e Innovacion de Espana (AP2007-00289). This paper was recommended by Associate Editor J. Huang.Alberola Oltra, JM.; Julian Inglada, VJ.; García-Fornes, A. (2013). Multi-dimensional adaptation in MAS organizations. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics. 43(2):622-633. https://doi.org/10.1109/TSMCB.2012.2213592S62263343

Reaching unanimous agreements within agent-based negotiation teams with linear and monotonic utility functions

[EN] In this article, an agent-based negotiation model for negotiation teams that negotiate a deal with an opponent is presented. Agent-based negotiation teams are groups of agents that join together as a single negotiation party because they share an interest that is related to the negotiation process. The model relies on a trusted mediator that coordinates and helps team members in the decisions that they have to take during the negotiation process: which offer is sent to the opponent, and whether the offers received from the opponent are accepted. The main strength of the proposed negotiation model is the fact that it guarantees unanimity within team decisions since decisions report a utility to team members that is greater than or equal to their aspiration levels at each negotiation round. This work analyzes how unanimous decisions are taken within the team and the robustness of the model against different types of manipulations. An empirical evaluation is also performed to study the impact of the different parameters of the model.This work is supported by TIN2008-04446, PROMETEO/2008/051, TIN2009-13839-C03-01, CSD2007-00022 of the Spanish government, and FPU Grant AP2008-00600 awarded to Victor Sanchez-Anguix. This paper was recommended by Associate Editor X. Wang.Sanchez-Anguix, V.; Julian Inglada, VJ.; Botti, V.; García-Fornes, A. (2012). Reaching unanimous agreements within agent-based negotiation teams with linear and monotonic utility functions. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics. 42(3):778-792. https://doi.org/10.1109/TSMCB.2011.2177658S77879242

Detection and nudge-intervention on sensitive information in social networks

[EN] Detecting sensitive information considering privacy is a relevant issue on Online Social Networks (OSNs). It is often difficult for users to manage the privacy associated with their posts on social networks taking into account all the possible consequences. The aim of this work is to provide information about the sensitivity of the content of a publication when a user is going to share it in OSN. For this purpose, we developed a privacy-assistant agent that detects sensitive information. Based on this information, the agent provides a message through a nudge mechanism warning about the possible risks of sharing the message. To avoid being annoying, the agent also considers the user's previous behaviour (e.g. if he previously ignored certain nudges) and adapts the messages it sends to give more relevance to those categories that are more important to the user from the point of view of the privacy risk. This agent was integrated into the social network Pesedia. We analysed the performance of different models to detect a set of sensitive categories (i.e. location, medical, drug/alcohol, emotion, personal attacks, stereotyping, family and association details, personal details and personally identifiable information) in a dataset of tweets in Spanish. The model that obtained the best results (i.e. F1 and accuracy) and that was finally integrated into the privacy-assistant agent was transformer-based.This work is supported by the Spanish Government project TIN2017-89156-R.Alemany, J.; Botti-Cebriá, V.; Del Val Noguera, E.; García-Fornes, A. (2022). Detection and nudge-intervention on sensitive information in social networks. Logic Journal of IGPL. 30(6):942-953. https://doi.org/10.1093/jigpal/jzac00494295330

Distributed goal-oriented computing

For current computing frameworks, the ability to dynamically use the resources that are allocated in the network has become a key success factor. As long as the size of the network increases, it is more difficult to find how to solve the problems that the users are presenting. Users usually do know what they want to do, but they do not know how to do it. If the user knows its goals it could be easier to help him with a different approach. In this work we present a new computing paradigm based on goals. This paradigm is called Distributed goal-oriented computing paradigm. To implement this paradigm an execution framework for a goal-oriented operating system has been designed. In this paradigm users express their goals and the OS is in charge of helping the achievement of these goals by means of a service-oriented approach. © 2012 Elsevier Inc. All rights reserved.This work is supported by TIN2008-04446 and TIN2009-13839-C03-01 projects of the Spanish Government, PROMETEO/2008/051 project, FEDER funds and CONSOLIDER-INGENIO 2010 under grant CSD2007-00022.Palanca Cámara, J.; Navarro Llácer, M.; Julian Inglada, VJ.; García-Fornes, A. (2012). Distributed goal-oriented computing. Journal of Systems and Software. 85(7):1540-1557. https://doi.org/10.1016/j.jss.2012.01.045S1540155785

A Review on MAS-Based Sentiment and Stress Analysis User-Guiding and Risk-Prevention Systems in Social Network Analysis

[EN] In the current world we live immersed in online applications, being one of the most present of them Social Network Sites (SNSs), and different issues arise from this interaction. Therefore, there is a need for research that addresses the potential issues born from the increasing user interaction when navigating. For this reason, in this survey we explore works in the line of prevention of risks that can arise from social interaction in online environments, focusing on works using Multi-Agent System (MAS) technologies. For being able to assess what techniques are available for prevention, works in the detection of sentiment polarity and stress levels of users in SNSs will be reviewed. We review with special attention works using MAS technologies for user recommendation and guiding. Through the analysis of previous approaches on detection of the user state and risk prevention in SNSs we elaborate potential future lines of work that might lead to future applications where users can navigate and interact between each other in a more safe way.This work was funded by the project TIN2017-89156-R of the Spanish government.Aguado-Sarrió, G.; Julian Inglada, VJ.; García-Fornes, A.; Espinosa Minguet, AR. (2020). A Review on MAS-Based Sentiment and Stress Analysis User-Guiding and Risk-Prevention Systems in Social Network Analysis. Applied Sciences. 10(19):1-29. https://doi.org/10.3390/app10196746S1291019Vanderhoven, E., Schellens, T., Vanderlinde, R., & Valcke, M. (2015). Developing educational materials about risks on social network sites: a design based research approach. Educational Technology Research and Development, 64(3), 459-480. doi:10.1007/s11423-015-9415-4Teens and ICT: Risks and Opportunities. Belgium: TIRO http://www.belspo.be/belspo/fedra/proj.asp?l=en&COD=TA/00/08Risks and Safety on the Internet: The Perspective of European Children: Full Findings and Policy Implications From the EU Kids Online Survey of 9–16 Year Olds and Their Parents in 25 Countries http://eprints.lse.ac.uk/33731/Vanderhoven, E., Schellens, T., & Valcke, M. (2014). Educating teens about the risks on social network sites. An intervention study in Secondary Education. Comunicar, 22(43), 123-132. doi:10.3916/c43-2014-12Christofides, E., Muise, A., & Desmarais, S. (2012). Risky Disclosures on Facebook. Journal of Adolescent Research, 27(6), 714-731. doi:10.1177/0743558411432635George, J. M., & Dane, E. (2016). Affect, emotion, and decision making. Organizational Behavior and Human Decision Processes, 136, 47-55. doi:10.1016/j.obhdp.2016.06.004Thelwall, M. (2017). TensiStrength: Stress and relaxation magnitude detection for social media texts. Information Processing & Management, 53(1), 106-121. doi:10.1016/j.ipm.2016.06.009Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), 2544-2558. doi:10.1002/asi.21416Shoumy, N. J., Ang, L.-M., Seng, K. P., Rahaman, D. M. M., & Zia, T. (2020). Multimodal big data affective analytics: A comprehensive survey using text, audio, visual and physiological signals. Journal of Network and Computer Applications, 149, 102447. doi:10.1016/j.jnca.2019.102447Zhang, C., Zeng, D., Li, J., Wang, F.-Y., & Zuo, W. (2009). Sentiment analysis of Chinese documents: From sentence to document level. Journal of the American Society for Information Science and Technology, 60(12), 2474-2487. doi:10.1002/asi.21206Lu, B., Ott, M., Cardie, C., & Tsou, B. K. (2011). Multi-aspect Sentiment Analysis with Topic Models. 2011 IEEE 11th International Conference on Data Mining Workshops. doi:10.1109/icdmw.2011.125Nasukawa, T., & Yi, J. (2003). Sentiment analysis. Proceedings of the international conference on Knowledge capture - K-CAP ’03. doi:10.1145/945645.945658Borth, D., Ji, R., Chen, T., Breuel, T., & Chang, S.-F. (2013). Large-scale visual sentiment ontology and detectors using adjective noun pairs. Proceedings of the 21st ACM international conference on Multimedia - MM ’13. doi:10.1145/2502081.2502282Deb, S., & Dandapat, S. (2019). Emotion Classification Using Segmentation of Vowel-Like and Non-Vowel-Like Regions. IEEE Transactions on Affective Computing, 10(3), 360-373. doi:10.1109/taffc.2017.2730187Deng, J., Zhang, Z., Marchi, E., & Schuller, B. (2013). Sparse Autoencoder-Based Feature Transfer Learning for Speech Emotion Recognition. 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction. doi:10.1109/acii.2013.90Nicolaou, M. A., Gunes, H., & Pantic, M. (2011). Continuous Prediction of Spontaneous Affect from Multiple Cues and Modalities in Valence-Arousal Space. IEEE Transactions on Affective Computing, 2(2), 92-105. doi:10.1109/t-affc.2011.9Hossain, M. S., Muhammad, G., Alhamid, M. F., Song, B., & Al-Mutib, K. (2016). Audio-Visual Emotion Recognition Using Big Data Towards 5G. Mobile Networks and Applications, 21(5), 753-763. doi:10.1007/s11036-016-0685-9Zhou, F., Jianxin Jiao, R., & Linsey, J. S. (2015). Latent Customer Needs Elicitation by Use Case Analogical Reasoning From Sentiment Analysis of Online Product Reviews. Journal of Mechanical Design, 137(7). doi:10.1115/1.4030159Ceci, F., Goncalves, A. L., & Weber, R. (2016). A model for sentiment analysis based on ontology and cases. IEEE Latin America Transactions, 14(11), 4560-4566. doi:10.1109/tla.2016.7795829Vizer, L. M., Zhou, L., & Sears, A. (2009). Automated stress detection using keystroke and linguistic features: An exploratory study. International Journal of Human-Computer Studies, 67(10), 870-886. doi:10.1016/j.ijhcs.2009.07.005Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89. doi:10.1145/2436256.2436274Schouten, K., & Frasincar, F. (2016). Survey on Aspect-Level Sentiment Analysis. IEEE Transactions on Knowledge and Data Engineering, 28(3), 813-830. doi:10.1109/tkde.2015.2485209Ji, R., Cao, D., Zhou, Y., & Chen, F. (2016). Survey of visual sentiment prediction for social media analysis. Frontiers of Computer Science, 10(4), 602-611. doi:10.1007/s11704-016-5453-2Li, L., Cao, D., Li, S., & Ji, R. (2015). Sentiment analysis of Chinese micro-blog based on multi-modal correlation model. 2015 IEEE International Conference on Image Processing (ICIP). doi:10.1109/icip.2015.7351718Lee, P.-M., Tsui, W.-H., & Hsiao, T.-C. (2015). The Influence of Emotion on Keyboard Typing: An Experimental Study Using Auditory Stimuli. PLOS ONE, 10(6), e0129056. doi:10.1371/journal.pone.0129056Matsiola, M., Dimoulas, C., Kalliris, G., & Veglis, A. A. (2018). Augmenting User Interaction Experience Through Embedded Multimodal Media Agents in Social Networks. Information Retrieval and Management, 1972-1993. doi:10.4018/978-1-5225-5191-1.ch088Rosaci, D. (2007). CILIOS: Connectionist inductive learning and inter-ontology similarities for recommending information agents. Information Systems, 32(6), 793-825. doi:10.1016/j.is.2006.06.003Buccafurri, F., Comi, A., Lax, G., & Rosaci, D. (2016). Experimenting with Certified Reputation in a Competitive Multi-Agent Scenario. IEEE Intelligent Systems, 31(1), 48-55. doi:10.1109/mis.2015.98Rosaci, D., & Sarnè, G. M. L. (2014). Multi-agent technology and ontologies to support personalization in B2C E-Commerce. Electronic Commerce Research and Applications, 13(1), 13-23. doi:10.1016/j.elerap.2013.07.003Singh, A., & Sharma, A. (2017). MAICBR: A Multi-agent Intelligent Content-Based Recommendation System. Lecture Notes in Networks and Systems, 399-411. doi:10.1007/978-981-10-3920-1_41Villavicencio, C., Schiaffino, S., Diaz-Pace, J. A., Monteserin, A., Demazeau, Y., & Adam, C. (2016). A MAS Approach for Group Recommendation Based on Negotiation Techniques. Lecture Notes in Computer Science, 219-231. doi:10.1007/978-3-319-39324-7_19Rincon, J. A., de la Prieta, F., Zanardini, D., Julian, V., & Carrascosa, C. (2017). Influencing over people with a social emotional model. Neurocomputing, 231, 47-54. doi:10.1016/j.neucom.2016.03.107Aguado, G., Julian, V., Garcia-Fornes, A., & Espinosa, A. (2020). A Multi-Agent System for guiding users in on-line social environments. Engineering Applications of Artificial Intelligence, 94, 103740. doi:10.1016/j.engappai.2020.103740Aguado, G., Julián, V., García-Fornes, A., & Espinosa, A. (2020). Using Keystroke Dynamics in a Multi-Agent System for User Guiding in Online Social Networks. Applied Sciences, 10(11), 3754. doi:10.3390/app10113754Camara, M., Bonham-Carter, O., & Jumadinova, J. (2015). A multi-agent system with reinforcement learning agents for biomedical text mining. Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics. doi:10.1145/2808719.2812596Lombardo, G., Fornacciari, P., Mordonini, M., Tomaiuolo, M., & Poggi, A. (2019). A Multi-Agent Architecture for Data Analysis. Future Internet, 11(2), 49. doi:10.3390/fi11020049Schweitzer, F., & Garcia, D. (2010). An agent-based model of collective emotions in online communities. The European Physical Journal B, 77(4), 533-545. doi:10.1140/epjb/e2010-00292-

Using Keystroke Dynamics in a Multi-Agent System for User Guiding in Online Social Networks

[EN] Nowadays there is a strong integration of online social platforms and applications with our daily life. Such interactions can make risks arise and compromise the information we share, thereby leading to privacy issues. In this work, a proposal that makes use of a software agent that performs sentiment analysis and another performing stress analysis on keystroke dynamics data has been designed and implemented. The proposal consists of a set of new agents that have been integrated into a multi-agent system (MAS) for guiding users interacting in online social environments, which has agents for sentiment and stress analysis on text. We propose a combined analysis using the different agents. The MAS analyzes the states of the users when they are interacting, and warns them if the messages they write are deemed negative. In this way, we aim to prevent potential negative outcomes on social network sites (SNSs). We performed experiments in the laboratory with our private SNS Pesedia over a period of one month, so we gathered data about text messages and keystroke dynamics data, and used the datasets to train the artificial neural networks (ANNs) of the agents. A set of experiments was performed for discovering which analysis is able to detect a state of the user that propagates more in the SNS, so it may be more informative for the MAS. Our study will help develop future intelligent systems that utilize user data in online social environments for guiding or helping them in their social experience.This work was funded by the project TIN2017-89156-R of the Spanish government.Aguado-Sarrió, G.; Julian Inglada, VJ.; García-Fornes, A.; Espinosa Minguet, AR. (2020). Using Keystroke Dynamics in a Multi-Agent System for User Guiding in Online Social Networks. Applied Sciences. 10(11):1-20. https://doi.org/10.3390/app10113754S1201011O’Keeffe, G. S., & Clarke-Pearson, K. (2011). The Impact of Social Media on Children, Adolescents, and Families. PEDIATRICS, 127(4), 800-804. doi:10.1542/peds.2011-0054George, J. M., & Dane, E. (2016). Affect, emotion, and decision making. Organizational Behavior and Human Decision Processes, 136, 47-55. doi:10.1016/j.obhdp.2016.06.004Thelwall, M. (2017). TensiStrength: Stress and relaxation magnitude detection for social media texts. Information Processing & Management, 53(1), 106-121. doi:10.1016/j.ipm.2016.06.009Aguado, G., Julian, V., & Garcia-Fornes, A. (2018). Towards Aiding Decision-Making in Social Networks by Using Sentiment and Stress Combined Analysis. Information, 9(5), 107. doi:10.3390/info9050107Schouten, K., & Frasincar, F. (2016). Survey on Aspect-Level Sentiment Analysis. IEEE Transactions on Knowledge and Data Engineering, 28(3), 813-830. doi:10.1109/tkde.2015.2485209Lee, P.-M., Tsui, W.-H., & Hsiao, T.-C. (2015). The Influence of Emotion on Keyboard Typing: An Experimental Study Using Auditory Stimuli. PLOS ONE, 10(6), e0129056. doi:10.1371/journal.pone.0129056Vizer, L. M., Zhou, L., & Sears, A. (2009). Automated stress detection using keystroke and linguistic features: An exploratory study. International Journal of Human-Computer Studies, 67(10), 870-886. doi:10.1016/j.ijhcs.2009.07.005Huang, F., Zhang, X., Zhao, Z., Xu, J., & Li, Z. (2019). Image–text sentiment analysis via deep multimodal attentive fusion. Knowledge-Based Systems, 167, 26-37. doi:10.1016/j.knosys.2019.01.019Mehrabian, A. (1996). Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in Temperament. Current Psychology, 14(4), 261-292. doi:10.1007/bf02686918Ulinskas, M., Damaševičius, R., Maskeliūnas, R., & Woźniak, M. (2018). Recognition of human daytime fatigue using keystroke data. Procedia Computer Science, 130, 947-952. doi:10.1016/j.procs.2018.04.09

Open challenges in relationship-based privacy mechanisms for social network services

[EN] Social networking services (SNSs) such as Facebook or Twitter have experienced an explosive growth during the few past years. Millions of users have created their profiles on these services because they experience great benefits in terms of friendship. SNSs can help people to maintain their friendships, organize their social lives, start new friendships, or meet others that share their hobbies and interests. However, all these benefits can be eclipsed by the privacy hazards that affect people in SNSs. People expose intimate information of their lives on SNSs, and this information affects the way others think about them. It is crucial that users be able to control how their information is distributed through the SNSs and decide who can access it. This paper presents a list of privacy threats that can affect SNS users, and what requirements privacy mechanisms should fulfill to prevent this threats. Then, we review current approaches and analyze to what extent they cover the requirementsThis article has been developed as a result of a mobility stay funded by the Erasmus Mundus Programme of the European Comission under the Transatlantic Partnership for Excellence in Engineering-TEE Project.López Fogués, R.; Such Aparicio, JM.; Espinosa Minguet, AR.; García-Fornes, A. (2015). Open challenges in relationship-based privacy mechanisms for social network services. International Journal of Human-Computer Interaction. 31(5):350-370. doi:10.1080/10447318.2014.1001300S35037031

Metrics for privacy assessment when sharing information in online social networks

(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.[EN] Privacy risk in Online Social Networks has become an important social concern. Users, with different perceptions of risk, share information without considering the audience that has access to the information disclosed or how far a publication will go. According to this, we propose two metrics (Audience and Reachability) based on information flows and friendship layers that indicate the privacy risk of sharing information, addressing the posts¿ scope and invisible audience. We assess these metrics through agent simulations in well-known models of networks. The findings show a strong relationship between metrics and structural centrality network properties. We also studied scenarios where there is no previous information about users activity or the information about the traces of the messages cannot be obtained. To deal with privacy assessment in these scenarios, we analyze the relationship between the proposed privacy metrics and local centrality properties as an estimation of privacy risk. The results showed that effectiveness centrality can be used as a suitable approximation of the proposed privacy measures.This work was supported in part by the Spanish Government project under Grant TIN2017-89156-R, and in part by the FPI under Grant BES-2015-074498.Alemany-Bordera, J.; Del Val Noguera, E.; Alberola Oltra, JM.; García-Fornes, A. (2019). Metrics for privacy assessment when sharing information in online social networks. IEEE Access. 7:143631-143645. https://doi.org/10.1109/ACCESS.2019.2944723S143631143645

Biochar versus hydrochar as growth media constituents for ornamental plant cultivation

[EN] Biochar and hydrochar have been proposed as novel materials for providing soilless growth media. However, much more knowledge is required before reliable advice can be given on the use of these materials for this purpose. Depending on the material and the technology applied (pyrolysis or hydrothermal carbonization), phytotoxicity and greenhouse gas emissions have been found for certain chars. In this study, our aim was to assess the feasibility of three chars as substrate constituents. We compared two biochars, one from forest waste and the other from olive mill waste, and a hydrochar from forest waste. We studied how chars affected substrate characteristics, plant performance, water economy and respiratory CO2 emission. Substrates containing biochar from forest waste showed the best characteristics, with good air/water relationships and adequate electrical conductivity. Those with biochar from olive mill waste were highly saline and, consequently, low quality. The substrates with hydrochar retained too much water and were poorly aerated, presenting high CO2 concentrations due to high respiratory activity. Plants performed well only when grown in substrates containing a maximum of 25 % biochar from forest waste or hydrochar. After analyzing the char characteristics, we concluded that biochar from forest waste could be safely used as a substrate constituent and is environmentally friendly when applied due to its low salinity and low CO2 emission. However, biochar from olive mill waste and hydrochar need to be improved before they can be used as substrate constituents.This study was funded by the Polytechnic University of Valencia (Projects on New Multidisciplinary Research; PAID-05-12). We thank Molly Marcus-McBride for supervising the English.Fornes Sebastiá, F.; Belda Navarro, RM. (2018). Biochar versus hydrochar as growth media constituents for ornamental plant cultivation. Scientia Agricola (Online). 75(4):304-312. https://doi.org/10.1590/1678-992X-2017-0062S304312754Abad, M., Noguera, P., & Burés, S. (2001). National inventory of organic wastes for use as growing media for ornamental potted plant production: case study in Spain. Bioresource Technology, 77(2), 197-200. doi:10.1016/s0960-8524(00)00152-8Bargmann, I., Martens, R., Rillig, M. C., Kruse, A., & Kücke, M. (2013). Hydrochar amendment promotes microbial immobilization of mineral nitrogen. Journal of Plant Nutrition and Soil Science, 177(1), 59-67. doi:10.1002/jpln.201300154Bargmann, I., Rillig, M. C., Buss, W., Kruse, A., & Kuecke, M. (2013). Hydrochar and Biochar Effects on Germination of Spring Barley. Journal of Agronomy and Crop Science, 199(5), 360-373. doi:10.1111/jac.12024Bedussi, F., Zaccheo, P., & Crippa, L. (2015). Pattern of pore water nutrients in planted and non-planted soilless substrates as affected by the addition of biochars from wood gasification. 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