Online Hate Speech against Women: Automatic Identification of Misogyny and Sexism on Twitter

[EN] Patriarchal behavior, such as other social habits, has been transferred online, appearing as misogynistic and sexist comments, posts or tweets. This online hate speech against women has serious consequences in real life, and recently, various legal cases have arisen against social platforms that scarcely block the spread of hate messages towards individuals. In this difficult context, this paper presents an approach that is able to detect the two sides of patriarchal behavior, misogyny and sexism, analyzing three collections of English tweets, and obtaining promising results.The work of Simona Frenda and Paolo Rosso was partially funded by the Spanish MINECO under the research project SomEMBED (TIN2015-71147-C2-1-P). We also thank the support of CONACYT-Mexico (project FC-2410).Frenda, S.; Ghanem, B.; Montes-Y-Gómez, M.; Rosso, P. (2019). Online Hate Speech against Women: Automatic Identification of Misogyny and Sexism on Twitter. Journal of Intelligent & Fuzzy Systems. 36(5):4743-4752. https://doi.org/10.3233/JIFS-179023S47434752365Anzovino M. , Fersini E. and Rosso P. , Automatic Identification and Classification of Misogynistic Language on Twitter, Proc 23rd International Conference on Applications of Natural Language to Information Systems, NLDB-2018, Springer-Verlag, LNCS 10859, 2018, pp. 57–64.Burnap P. and Williams M.L. , Hate speech, machine classification and statistical modelling of information flows on Twitter: Interpretation and communication for policy decision making, Internet, Policy and Politics, Oxford, UK, 2014.Burnap, P., Rana, O. F., Avis, N., Williams, M., Housley, W., Edwards, A., … Sloan, L. (2015). Detecting tension in online communities with computational Twitter analysis. Technological Forecasting and Social Change, 95, 96-108. doi:10.1016/j.techfore.2013.04.013Chen Y. , Zhou Y. , Zhu S. and Xu H. , Detecting offensive language in social media to protect adolescent online safety, Privacy, Security, Risk and Trust (PASSAT), 2012 International Conference on and 2012 International Conference on Social Computing (SocialCom), Amsterdam, Netherlands, IEEE, 2012, pp. 71–80.Escalante, H. J., Villatoro-Tello, E., Garza, S. E., López-Monroy, A. P., Montes-y-Gómez, M., & Villaseñor-Pineda, L. (2017). Early detection of deception and aggressiveness using profile-based representations. Expert Systems with Applications, 89, 99-111. doi:10.1016/j.eswa.2017.07.040Fersini E. , Anzovino M. and Rosso P. , Overview of the Task on Automatic Misogyny Identification at IBEREVAL, CEUR Workshop Proceedings 2150, Seville, Spain, 2018.Fersini E. , Nozza D. and Rosso P. , Overview of the Evalita 2018 Task on Automatic Misogyny Identification (AMI), Proceedings of the 6th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA’18), Turin, Italy, 2018.Fox, J., & Tang, W. Y. (2014). Sexism in online video games: The role of conformity to masculine norms and social dominance orientation. Computers in Human Behavior, 33, 314-320. doi:10.1016/j.chb.2013.07.014Fulper R. , Ciampaglia G.L. , Ferrara E. , Ahn Y. , Flammini A. , Menczer F. , Lewis B. and Rowe K. , Misogynistic language on Twitter and sexual violence, Proceedings of the ACM Web Science Workshop on Computational Approaches to Social Modeling (ChASM), 2014.Gambäck B. and Sikdar U.K. , Using convolutional neural networks to classify hate-speech, Proceedings of the First Workshop on Abusive Language Online 2017.Hewitt, S., Tiropanis, T., & Bokhove, C. (2016). The problem of identifying misogynist language on Twitter (and other online social spaces). Proceedings of the 8th ACM Conference on Web Science. doi:10.1145/2908131.2908183Justo R. , Corcoran T. , Lukin S.M. , Walker M. and Torres M.I. , Extracting relevant knowledge for the detection of sarcasm and nastiness in the social web, Knowledge-Based Systems, 2014.Lapidot-Lefler, N., & Barak, A. (2012). Effects of anonymity, invisibility, and lack of eye-contact on toxic online disinhibition. Computers in Human Behavior, 28(2), 434-443. doi:10.1016/j.chb.2011.10.014Nobata C. , Tetreault J. , Thomas A. , Mehdad Y. and Chang Y. , Abusive language detection in online user content, Proceedings of the 25th International Conference on World Wide Web, Geneva, Switzerland, 2016, pp. 145–153.Poland, B. (2016). Haters. doi:10.2307/j.ctt1fq9wdpSamghabadi N.S. , Maharjan S. , Sprague A. , Diaz-Sprague R. and Solorio T. , Detecting nastiness in social media, Proceedings of the First Workshop on Abusive Language Online, Vancouver, Canada, 2017, pp. 63–72. Association for Computational Linguistics.Sood, S., Antin, J., & Churchill, E. (2012). Profanity use in online communities. Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems - CHI ’12. doi:10.1145/2207676.220861

Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field

Exploring Maintainability Assurance Research for Service- and Microservice-Based Systems: Directions and Differences

To ensure sustainable software maintenance and evolution, a diverse set of activities and concepts like metrics, change impact analysis, or antipattern detection can be used. Special maintainability assurance techniques have been proposed for service- and microservice-based systems, but it is difficult to get a comprehensive overview of this publication landscape. We therefore conducted a systematic literature review (SLR) to collect and categorize maintainability assurance approaches for service-oriented architecture (SOA) and microservices. Our search strategy led to the selection of 223 primary studies from 2007 to 2018 which we categorized with a threefold taxonomy: a) architectural (SOA, microservices, both), b) methodical (method or contribution of the study), and c) thematic (maintainability assurance subfield). We discuss the distribution among these categories and present different research directions as well as exemplary studies per thematic category. The primary finding of our SLR is that, while very few approaches have been suggested for microservices so far (24 of 223, ?11%), we identified several thematic categories where existing SOA techniques could be adapted for the maintainability assurance of microservices

Interactive spaces for children: gesture elicitation for controlling ground mini-robots

[EN] Interactive spaces for education are emerging as a mechanism for fostering children's natural ways of learning by means of play and exploration in physical spaces. The advanced interactive modalities and devices for such environments need to be both motivating and intuitive for children. Among the wide variety of interactive mechanisms, robots have been a popular research topic in the context of educational tools due to their attractiveness for children. However, few studies have focused on how children would naturally interact and explore interactive environments with robots. While there is abundant research on full-body interaction and intuitive manipulation of robots by adults, no similar research has been done with children. This paper therefore describes a gesture elicitation study that identified the preferred gestures and body language communication used by children to control ground robots. The results of the elicitation study were used to define a gestural language that covers the different preferences of the gestures by age group and gender, with a good acceptance rate in the 6-12 age range. The study also revealed interactive spaces with robots using body gestures as motivating and promising scenarios for collaborative or remote learning activities.This work is funded by the European Development Regional Fund (EDRF-FEDER) and supported by the Spanish MINECO (TIN2014-60077-R). The work of Patricia Pons is supported by a national grant from the Spanish MECD (FPU13/03831). Special thanks are due to the children and teachers of the Col-legi Public Vicente Gaos for their valuable collaboration and dedication.Pons Tomás, P.; Jaén Martínez, FJ. (2020). Interactive spaces for children: gesture elicitation for controlling ground mini-robots. 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News Session-Based Recommendations using Deep Neural Networks

News recommender systems are aimed to personalize users experiences and help them to discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, fast growing number of items, accelerated item's value decay, and users preferences dynamic shift. Some promising results have been recently achieved by the usage of Deep Learning techniques on Recommender Systems, specially for item's feature extraction and for session-based recommendations with Recurrent Neural Networks. In this paper, it is proposed an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News Recommender Systems. This architecture is composed of two modules, the first responsible to learn news articles representations, based on their text and metadata, and the second module aimed to provide session-based recommendations using Recurrent Neural Networks. The recommendation task addressed in this work is next-item prediction for users sessions: "what is the next most likely article a user might read in a session?" Users sessions context is leveraged by the architecture to provide additional information in such extreme cold-start scenario of news recommendation. Users' behavior and item features are both merged in an hybrid recommendation approach. A temporal offline evaluation method is also proposed as a complementary contribution, for a more realistic evaluation of such task, considering dynamic factors that affect global readership interests like popularity, recency, and seasonality. Experiments with an extensive number of session-based recommendation methods were performed and the proposed instantiation of CHAMELEON meta-architecture obtained a significant relative improvement in top-n accuracy and ranking metrics (10% on Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada. https://recsys.acm.org/recsys18/dlrs

Case study:exploring children’s password knowledge and practices

Children use technology from a very young age, and often have to authenticate themselves. Yet very little attention has been paid to designing authentication specifically for this particular target group. The usual practice is to deploy the ubiquitous password, and this might well be a suboptimal choice. Designing authentication for children requires acknowledgement of child-specific developmental challenges related to literacy, cognitive abilities and differing developmental stages. Understanding the current state of play is essential, to deliver insights that can inform the development of child-centred authentication mechanisms and processes. We carried out a systematic literature review of all research related to children and authentication since 2000. A distinct research gap emerged from the analysis. Thus, we designed and administered a survey to school children in the United States (US), so as to gain insights into their current password usage and behaviors. This paper reports preliminary results from a case study of 189 children (part of a much larger research effort). The findings highlight age-related differences in children’s password understanding and practices. We also discovered that children confuse concepts of safety and security. We conclude by suggesting directions for future research. This paper reports on work in progress.<br/

FogGIS: Fog Computing for Geospatial Big Data Analytics

Cloud Geographic Information Systems (GIS) has emerged as a tool for analysis, processing and transmission of geospatial data. The Fog computing is a paradigm where Fog devices help to increase throughput and reduce latency at the edge of the client. This paper developed a Fog-based framework named Fog GIS for mining analytics from geospatial data. We built a prototype using Intel Edison, an embedded microprocessor. We validated the FogGIS by doing preliminary analysis. including compression, and overlay analysis. Results showed that Fog computing hold a great promise for analysis of geospatial data. We used several open source compression techniques for reducing the transmission to the cloud.Comment: 6 pages, 4 figures, 1 table, 3rd IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (09-11 December, 2016) Indian Institute of Technology (Banaras Hindu University) Varanasi, Indi

Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201