A Recommender System based on Tweets for points ...

Social Recommender System: A Recommender System based on Tweets for points of interest Cristian González García

Daniel Meana-Llorián

University of Oviedo Model-Driven Engineering Research Group Department of Computer Science Oviedo, Asturias, Spain +34 985 10 33 97


[email protected] Vicente García-Díaz

[email protected] Edward Rolando Núñez-Valdez



[email protected]

[email protected]

ABSTRACT Social networks are an inexhaustible source of knowledge. One of its main uses is to express opinions about a particular product or service. Thus, users usually write opinions about places or so-called points of interest. That is a valuable knowledge for other users and especially for users who know and trust the opinions of the writers. In this work, we present an overview of a platform to capture opinions related to places and generate a positive or a negative recommendation for users based on both the global use of Twitter as well as the comments made by trusted people on the network. CSS Concepts • Information applications;

systems

➝

Information

systems

• Information systems ➝ World Wide Web ➝ Web applications ➝ Social networks; • Information systems ➝ Information systems applications ➝ Collaborative and social computing systems and tools ➝ Social networking sites; • Computing methodologies ➝ Machine learning;

Keywords Recommender Systems; Online Social Networks; Twitter; Artificial Intelligence; Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is

permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. MISNC '17, July 17-19, 2017, Bangkok, Thailand © 2017 Copyright is held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 978-1-4503-4881-2/17/07…$15.00 http://dx.doi.org/10.1145/3092090.3092121

1. INTRODUCTION When we use web pages like Amazon, Netflix, HBO or AliExpress, we receive recommendations of content that we could like to watch. The reason is simple: they are using a Recommender System that analyzes and suggests us this type of contents. Notwithstanding, some Recommender Systems need that users give a score or write a review, in addition to other metadata. This is something that the majority of users do not do. Users usually write posts on Online Social Networks (OSN) [1], where they explain to all their friends or even to the world their opinion about something. This is why it is very interesting to gather these posts to obtain this relevant data. The most used OSN to collect this type of information is Twitter [2]. On Twitter, people write their opinion using less than 140 characters to the entire world. Therefore, Twitter is a niche of opportunities to obtain open data from any user of this OSN, their comments and the evaluation of them, collecting a new relevant source of opinions for Recommender Systems. The problem, in this case, is how to analyse all the amount of this information. In this proposal, we present the main idea and different Artificial Intelligence-based approaches to try to solve this problem and create or

improve Recommender Systems that are based on Twitter general and personalized data. The remainder of this work is structured as follows: Section 2 presents the state of the art of the most important technologies to achieve our goals. Section 3 shows the proposal and general ideas to fit with our goals. Finally, Section 4 and Section 5 presents the conclusions and future work to be done respectively.

2. STATE OF THE ART In this section we give some hints of the state of the art on technologies that we apply in achieving our goals: 1) Recommender Systems; 2) Artificial Intelligence; and 3) Social Networks. The final proposal combines the three concepts.

2.1 Recommender Systems Recommender systems are intelligent systems that help users find the information they really need in an easy and efficient way. On the Internet, especially in e-commerce, social network and other types of websites, these systems help to reduce the time that users need to find content or products that are of interest to them. Recommender systems appear among other reasons with the purpose to improve web information overload and ease information recovery. This kind of systems aid users to find contents in a non-difficult way and with minimal effort [3]. As shown in several works [4], [6] the main problem that recommender systems try to solve is the data overload on the web. Through the implementation of algorithms and information classification mechanisms, these systems facilitate the information filtering process and therefore help users to find content in a faster and more accurate way. According to the information filtering paradigm that is used, in general, recommendation systems can be classified in several approaches that are: Collaborative filtering, Content-based and Hybrid Approad. Other authors such as Adomavicius and Tuzhilin [7] also propose other classifications of recommender systems, such as Utility based recommendation, Knowledge based recommendation and Demographic recommendation. In order for recommendation systems to be effective and provide valid information to users, such systems must collect information related to user’s profiles. This information is retrieved through feedback techniques. These techniques are classified into two types: Explicit and Implicit feedback. When these two feedback techniques are mixed, another paradigm for recommender systems is provided. For example, in Núñez-Valdez et al. [3], authors present an approach where a recommender system can assist users in finding electronic books in a social network using mixed feedback techniques.

A lot of e-commerce web sites as Amazon store, AliExpress and others types of websites as Film affinity, Netflix and HBO have a recommender system to offer interesting content to its users. Finally, social networks as Twiter, Facebook, YouTube, LinkedIn and Google+ also have this kind of intelligent systems to help users find friends, jobs and content that may be of interest to them.

2.2 Artificial Intelligence Artificial Intelligence (AI) is a concept that refers to the intelligence exhibited by machines or intelligent agents, i.e., any device that perceives its environment and performs actions that maximize its chance of success from the point of view of some specific goal [8]. AI is a huge and interdisciplinary field founded on the claim that human intelligence can be so precisely described that a machine can be made to mimic it [9]. Thus, AI is based on a range of different disciplines such as computer science, biology, psychology, linguistics, mathematics or sociology. To achieve its goal, IA usually relies on different approaches. For example, search and optimization algorithms are important for reasoning and lead from premises to conclusions. Fuzzy logic is a version of firstorder logic which allows the truth of a statement to be represented as a value between 0 and 1, rather than just 1 or 0. Probabilistic methods (e.g., Bayesian networks, Hidden Markov model, etc.) are very interesting for uncertain reasoning. Classifiers and statistical learning methods such as machine learning give computers the ability to learn without being explicitly programmed. Neural networks are computational models used to solve problems in the same way that the human brain would do. There are plenty of areas in which AI can be applied. For example, KL-ONE is a well-known system that has been used in both basic research and implemented knowledgebases systems for representing knowledge [10]. Atabakhsh [11] presented a survey of constraint based scheduling systems using an AI approach, trying to find feasible solutions which satisfies a maximum number of constrains within a reasonable amount of time. Korbicz et al. [12] worked on the development of fault diagnosis systems for real systems that can malfunction and fail due to problems in their components. Koller and Milch [13] defined multi-agent influence diagrams, which represent games in a way that allows to take advantage of independence relationships among variables for solving such games. Natural language processing is another interesting area in which AI has enabled advances with a significant number of publications on how to process and understand text written in natural language [14]. Other works such as Ramos et al. [15] explain why ambient intelligence will be the next step for AI with vehicles, robots, hospitals, tourist attractions, homes, factories,

offices, classrooms, etc. making use of AI to achieve more friendly and useful environments for people. One classic application of AI is to support recommendation systems. There are clear examples of intelligent systems. For example, Pazzani and Billsus [16] summarizes different techniques used in content-based recommendation systems such as user models, decision trees, nearest neighbor methods, Rocchio’s Algorithm, linear classifiers or probabilistic methods such as Naïve Bayes, all of them with their advantages and disadvantages. However, there are plenty of other AIbased techniques used on recommendation systems. For example, Cao and Li [17] proposed a system based on the specific needs of each user obtained from their interactions with the system and using fuzzy rules to recommend electronics products. Another current and rising use of AI is on the Web. Thus, some authors work on adaptive web searchers like Sugiyame et al. [18], who based on user profiles, propose several algorithms that can be used to adapt the results. Other works on adaptive web sites, or adaptive aspects of web sites. For example, Montserrat et al. [19] presented a generic and adaptive gamification system that can be used on different learning environments, automatically personalizing using analysis of interactions. Social networks, recommendation systems and AI can also work together. An interesting work is the one by Walter et al. [20], in which they propose a model of a trust-based recommendation system on a social network. The main idea is that users can take advantage of their social networks to reach information that they trust. Others are based on specific networks like Twitter. For example, TWILITE is a recommendation system for Twitter using a probabilistic model to recommend users to follow [21]. Otsuka et al. [22] propose a recommendation system scheme (Hashtag Frequency-Inverse Hashtag Ubiquity) to recommend the use of different hashtags for the tweets. Other works, such as Jonnalagedda and Gauch [23] use Twitter to recommend and suggest news to users based on their popularity as well as their relevance to the user profile.

2.3 Social Networks A Social Network can be any social structure composed of different actors, people or organisations, that have some type of relationship between themselves like a familiar relationship, work relationship, friend relationship, and so on. However, in this work we focus on Online Social Networks (OSN). These ones are based on the same idea but moved to the Internet. They have different names in the literature like Online Social Networks (OSN), Social Network Sites (SNS), or Social Networking Sites (SNS) [1], and are very important to the Web 2.0 because they are very necessary to achieve the convergence between the real world and the digital world [24].

The socialisation between people through the Internet is possible due to the several capacities of the OSNs like sending messages to friends, generating content made by users, sharing many types of contents, surfing profiles of users’ friends, and many other features. Continually, millions of citizens are sharing their thoughts, feelings, opinions, experiences, and so on, about anything that happens in their lives. The Online Social Networks are very used for research proposes because they are growing in popularity due to the ease of using them, their near-real time approach, and their scope [1], [25]. Nevertheless, OSN are used not only to be in touch with people and to research but also, they are used to advertising. Good opinions about a product in Social Networks can be a good indicator of its performance in the real-world [25]. Nowadays, research is focused principally on Twitter followed by Facebook. These Social Networks usually offer services to third developers through their APIs in order to create new applications that use them. Some services are authentication services, access control, recollection of data, and the integration of new functionalities in the Social Network through third applications. In this proposal, we will use Twitter as Social Network due to its features. Twitter is a Social Network and microblogging service based on short messages of up 140 characters called tweets. Users can use these messages as they want. For example, the can write about their feelings, stories, jokes, games, news, and so on [25], [26]. It has a specialized mark-up language that adds semantic information to messages and facilitates processing messages, based on using the symbols “@” to mention another user and “#” to mention a specific topic. Moreover, the relationships between users do not need reciprocation [27]. It means that users can follow other users without these users follow back and each user has a set of followers and another set of followed users. Both sets are completely independent.

3. PROPOSAL In the following lines, we are going to explain our proposal about making recommendations based on users’ posts on Social Networks. This section will address how our proposal will work and the different solutions that we can consider achieving our goals. Our principal goal is to create a novel system which will be able to collect users’ messages on Social Networks that reflect their opinions about different places. Then, process them in order to get a score for each place, and finally, making recommendations to users according the calculated scores and the opinions from users that the users follow. Thus, our proposal could be considered as a novel system of recommendations for places based on Social Networks. So, we are proposing a collaborative filtering recommendation system where the information needed to

make recommendations is retrieved through implicit feedback. This section is composed by four different subsections: 1) a section about how we will use Twitter to retrieve the information needed to make recommendations; 2) a section about the registration of places; 3) a section about the interaction of users with the proposed system; and 4) a section about the different possible implementations of the score calculator whose goal is to obtain a score by each place in an intelligent way.

3.1 Feedback from Twitter Our proposal will use Twitter as Social Network since it is a Social Network without reciprocation and most users post content openly. We want to make recommendations about a query made by a user through a web application. These recommendations will be made according to two sources of information: all posts available on Twitter, and posts from users followed by the user.

3.1.1 Public Twitter As we already said, the recommendations will be based on two sources of information. One of that will be all posts from Twitter that contain references to a place which will be registered in the system. Our goal is to calculate a score per registered place in order to make a ranking of recommended places that accomplish a query perform by a user. For example, if a user made a query about restaurants near Central Park in New York, the system would recommend restaurants registered in our systems that have good opinions on Twitter. For this purpose, we want to process all available messages from Twitter that mention places registered in our system to calculate a score for each place. To obtain a score per place we will apply AI techniques that will enable us to assign a score per message. We will talk about some possible techniques to apply in a next section.

3.1.2 Followed users The other source of information is the messages from users that the user that made the query follows. To make a recommendation, we need information relative to the user. In this case, we will use the users that the user follows because their opinions may be more relevant. Thus, our recommendation system is based on collaborative filtering and implicit feedback. From the information retrieved from followed users, the system will calculate another score per place which will be valid only for that user. This score will be calculated like the previous score. The system will recollect the tweets from followed users and it will apply AI techniques to obtain a score. Finally, the system will present a ranking of places to the user that fit the request made by mixing the general score obtained from all the messages on Twitter and the score

obtained from followed users’ messages. Thus, the presented ranking will be different amongst different users.

3.2 Registration of points of interest The identification of places (a.k.a., points of interest or POIs in some contexts) is an important issue to address. Our proposal will have to provide a mechanism to identify places on tweets although, before that, the system would be able to access a list of places. We identified three possibilities to register places: 1) manually; 2) automatically on demand; and 3) predictive and automated way. 

Manually

In order to have a record of places that the system will have to recognise in Twitter, we could create a form in the web application that users could use to register new places. Nevertheless, this way would have some advantages and disadvantages. The principal advantage is that the places will be already registered before a user made a query. However, the manual registration could produce that the number of places would be very limited and the information could be wrong because of registration errors. 

Automatically on demand

On the other side, the registration of places could be automatic but on demand. This means that the information of a place would be collected from external services like Google Places but the process of recollecting data would begin when a user made a query. For instance, when a user will make a query about restaurants in the centre of San Francisco, the system will search all places that accomplish these conditions in Google Places and match with the data stored in our proposal. However, if there is no data about a place, the system will register this place in order to begin to recollect tweets about this one. 

Predictive and automated way

Another approach could be the predictive and automated one. This approach would be complementary to the previous one because its goal is to collect places that users will search in the future automatically through services as Google Places. In order to achieve this, we need to identify possible future searches. For instance, if the system received many queries about places of the same city, the system would identify this city as a potential location for future queries, and thus, the system would collect places of other categories from Google Places automatically.

3.3 Users interface Our proposal consists on a system that recommends places according to a query made by a user with an account in Twitter. To make the query, the user will have to access to a web application which will have a form to

fill in. Through this form, the system will know what the user wants. The queries would be composed of a location, a distance, type of place, and other filters that will enable the system to do accurate recommendations.

of each user, because each person is different. Nevertheless, here we would have to create custom rules to adapt the final score of all the Tweets for each user. The other option is to use general rules for all users.

Users must have a Twitter account in order to enable the system to know the users that they follow. Thus, to use the form, users must login with their Twitter accounts.

4. CONCLUSIONS

Finally, the system will show a ranking of recommend POIs with their global score and personal score.

3.4 Different Solutions for the Score Calculator There are different possible solutions to obtain the evaluation of messages. We can think of two broad approaches: The first option is a lexical based approach by analyzing keywords. First of all, this method would search the Hashtag (#) and Mention (@) of the corresponding places to obtain the Tweets of the specific place. After it, it analyzes the rest of the words of each Tweet in search of other keywords that can allow evaluating the Tweet. These keywords could be: good, bad, very good, very bad, worst, best, or another word that gives a clear opinion about the place. With the combination of these keywords, we could give a score to the post and do the average between all the Tweets localized using the Hashtag and the Mentions. However, probably, this method is the less reliable of all the proposed in this article, but the simplest of implement. Another option is a semantic based approach by using Natural Language Processing (NLP) [28], [29]. NLP could be used to analyze all the Tweets that are possibly talking about the specific place. This method is similar to the previous one. Notwithstanding, in this case, the analyzer has to analyze all the words of the Tweet, obtaining in this way probably a better result, but needing more computation. Here, we will need the levels 3 to 6 according to González García [30], in the same way that the authors have proposed in some other works [31], [32]. NLP based methods are usually very efficient and rely on different machine learning technologies that give computers the ability to learn without being explicitly programmed. For that, different methods are used: Bayesian networks, artificial neural networks, decision trees, association rule learning, support vector machines, clustering, reinforcement learning, representation learning, genetic algorithms, etc. In addition, there are also works that focus on the creation of grammars to process texts for different contexts, instead of training a classifier with a training set composed of hundreds, thousands or even millions of data items, as it is usually required by machine learning algorithms. Finally, another of the concepts that will be managed is Fuzzy Logic [33] in order to obtain the final score, evaluating the words and combining the different words

With this proposal, we want to define the idea of creating a Recommend System using Tweets that people publish on Twitter, allowing them to send a query about a POI obtaining recommendations about it based on different criteria. This novel idea would enable to recollect relevant data from users that do not use the current systems to value restaurants, shops, or any other places, but to write their opinions in OSN. This is why this Recommend System would be able to obtain important comments with relevant information that could be analyzed to value and recommend places to users. In this case, the Recommend System will have two scores: the first one with a general score, which will be calculated using all the posts, and the second one with a more personal score, which will use the opinions of the users that are followed on Twitter by the user who wants the recommendation. This will offer the possibility of knowing the score based on all users of Twitter and based on the closer users that are followed on Twitter by the user who does the query. Then, this idea could improve the Recommend Systems using personal comments of any user who writes in Twitter. However, sometimes these comments contain emoticons, pictures or abbreviations that could improve the meaning, or give us more messages to analyze, but this part will need more research and the use of other technologies like Computer Vision, Machine Learning and Artificial Neural Networks.

5. Future Work The implementation of this Recommend System needs different branches of the Computer Science fields and have many different ways to continue the work. Next, we show some of these possibilities: 



Comparison among the different Artificial Intelligence techniques: in this article, we propose different solutions to achieve the goal, but maybe, when we will implement the final solution, we will not know which is the best one due to the amount of data that we will have to analyze. Then, a future work could be the implementation of several prototypes to create different measures to evaluate in terms of performance, accuracy, time-keeping, and more. Applying Big Data: due to the necessity of collecting many data, probably it will be necessary to apply some Big Data techniques and infrastructures to store and analyze quickly the necessary data.



Analyzing emoticons, pictures and abbreviations: sometimes comments contain emoticons, pictures or abbreviations that can change the meaning of the messages, or even they are used all alone and give the complete meaning of the messages. For instance, on Instagram, people sometimes only post pictures. On the other hand, in Twitter, due to the 140character limitation people use abbreviations or emoticons. Therefore, it is very important to analyze these three things to obtain more relevant meanings in the messages and even gather more messages.

ACKNOWLEDGMENTS This work was performed by the "Ingeniería Dirigida por Modelos MDE-RG" research group at the University of Oviedo under Contract No. FC-15-GRUPIN14-084 of the research project "Ingeniería Dirigida Por Modelos MDERG". Project financed by PR Proyecto Plan Regional.

[8]

D. Poole, A. Mackworth, and R. Goebal, Computational Intelligence: A Logical Approach. New York, New York, USA: Oxford University Press, New York, 1998.

[9]

R. R. Trippi and E. Turban, Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real World Performance. New York, NY, USA: McGraw-Hill, Inc., 1992.

[10]

R. J. Brachman and J. G. SchmolzeE, “An overview of the KL-ONE: Knowledge Representation System,” Cogn. Sci., vol. 9, no. 2, pp. 171–216, Jun. 1985.

[11]

H. Atabakhsh, “A survey of constraint based scheduling systems using an artificial intelligence approach,” Artif. Intell. Eng., vol. 6, no. 2, pp. 58– 73, Apr. 1991.

[12]

J. Korbicz, J. M. Koscielny, Z. Kowalczuk, and W. Cholewa, Fault Diagnosis: Models, Artificial Intelligence, Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.

REFERENCES [1]

Danah m. Boyd and N. B. Ellison, “Social Network Sites: Definition, History, and Scholarship,” J. Comput. Commun., vol. 13, no. 1, pp. 210–230, Oct. 2007.

[13]

D. Koller and B. Milch, “Multi-agent influence diagrams for representing and solving games,” Games Econ. Behav., vol. 45, no. 1, pp. 181–221, Oct. 2003.

[2]

Twitter Inc., “Twitter: Company,” 2016. [Online]. Available: https://about.twitter.com/es/company. [Accessed: 09-Jun-2016].

[14]

C. D. Manning and H. Schütze, Foundations of Statistical Natural Language Processing. London, England: The MIT Press, 1999.

[3]

E. R. Núñez-Valdez, J. M. C. Lovelle, G. I. Hernández, A. J. Fuente, and J. Labra-Gayo, “Creating recommendations on electronic books: A collaborative learning implicit approach,” Computers in Human Behavior, 2015.

[15]

C. Ramos, J. C. Augusto, and D. Shapiro, “Ambient Intelligence - the Next Step for Artificial Intelligence,” IEEE Intell. Syst., vol. 23, no. 2, pp. 15–18, Mar. 2008.

[4]

J. O’Donovan and B. Smyth, “Trust in recommender systems,” in Proceedings of the 10th international conference on Intelligent user interfaces, 2005, pp. 167–174.

[16]

M. J. Pazzani and D. Billsus, “Content-Based Recommendation Systems,” in The Adaptive Web, vol. 4321, P. Brusilovsky, A. Kobsa, and W. Nejdl, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 325–341.

[5]

P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens: an open architecture for collaborative filtering of netnews,” in Proceedings of the 1994 ACM conference on Computer supported cooperative work, 1994, pp. 175–186.

[17]

Y. Cao and Y. Li, “An intelligent fuzzy-based recommendation system for consumer electronic products,” Expert Syst. Appl., vol. 33, no. 1, pp. 230–240, Jul. 2007.

[18]

K. Sugiyama, K. Hatano, and M. Yoshikawa, “Adaptive web search based on user profile constructed without any effort from users,” in Proceedings of the 13th conference on World Wide Web - WWW ’04, 2004, p. 675.

[19]

B. Monterrat, E. Lavoué, and S. George, “Motivation for Learning: Adaptive Gamification for Web-based Learning Environments,” in 6th International Conference on Computer Supported Education, 2014, pp. 117–125.

[20]

F. E. Walter, S. Battiston, and F. Schweitzer, “A model of a trust-based recommendation system on a social network,” Auton. Agent. Multi. Agent.

[6]

[7]

R. González Crespo, O. Sanjuán Martínez, J. M. Cueva Lovelle, B. C. Pelayo García-Bustelo, J. E. L. Gayo, and P. O. de Pablo, “Recommendation System based on user interaction data applied to intelligent electronic books,” Comput. Human Behav., vol. In Press, p. , 2010. G. Adomavicius and A. Tuzhilin, “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions,” IEEE Trans. Knowl. Data Eng., vol. 17, no. 6, pp. 734–749, 2005.

Social Networks,” in Proceedings of the The 3rd Multidisciplinary International Social Networks Conference on SocialInformatics 2016, Data Science 2016 - MISNC, SI, DS 2016, 2016, pp. 1– 5.

Syst., vol. 16, no. 1, pp. 57–74, Feb. 2008. [21]

[22]

Y. Kim and K. Shim, “TWILITE: A recommendation system for Twitter using a probabilistic model based on latent Dirichlet allocation,” Inf. Syst., vol. 42, pp. 59–77, Jun. 2014.

[32]

E. Otsuka, S. A. Wallace, and D. Chiu, “Design and evaluation of a Twitter hashtag recommendation system,” in Proceedings of the 18th International Database Engineering & Applications Symposium on - IDEAS ’14, 2014, pp. 330–333.

D. Meana-Llorián, C. González García, B. C. Pelayo G-Bustelo, and N. García-Fernández, “SenseQ: Replying questions of Social Networks users by using a Wireless Sensor Network based on sensor relationships,” DATA Sci. PATTERN Recognit., vol. 1, no. 1, pp. 1–12, 2017.

[33]

L. A. Zadeh, “Fuzzy logic and approximate reasoning,” Synthese, vol. 30, no. 3–4, pp. 407– 428, 1975.

[23]

N. Jonnalagedda and S. Gauch, “Personalized News Recommendation Using Twitter,” in 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013, vol. 3, pp. 21–25.

[24]

M. Blackstock, R. Lea, and A. Friday, “Uniting online social networks with places and things,” in Proceedings of the Second International Workshop on Web of Things - WoT ’11, 2011, p. 1.

[25]

S. Asur and B. A. Huberman, “Predicting the Future with Social Media,” in 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2010, vol. 112, pp. 492–499.

[26]

T. Sakaki, M. Okazaki, and Y. Matsuo, “Earthquake shakes Twitter users: real-time event detection by social sensors,” in Proceedings of the 19th international conference on World wide web - WWW ’10, 2010, p. 851.

[27]

H. Kwak, C. Lee, H. Park, and S. Moon, “What is Twitter, a social network or a news media?,” in Proceedings of the 19th international conference on World wide web - WWW ’10, 2010, p. 591.

[28]

B. Z. Manaris and W. D. Dominick, “NALIGE: a user interface management system for the development of natural language interfaces,” Int. J. Man. Mach. Stud., vol. 38, no. 6, pp. 891–921, Jun. 1993.

[29]

B. Z. Manaris and B. M. Slator, “Interactive Natural Language Processing: Building on Success,” Computer (Long. Beach. Calif)., vol. 29, no. 7, pp. 28–32, Jul. 1996.

[30]

C. González García, “MIDGAR: Interoperabilidad de objetos en el marco de Internet de las Cosas mediante el uso de Ingeniería Dirigida por Modelos,” University of Oviedo, 2017.

[31]

D. Meana-Llorián, C. González García, V. García-Díaz, B. C. P. G-Bustelo, and J. M. C. Lovelle, “SenseQ: Creating relationships between objects to answer questions of humans by using