Predictive Analysis of Facebook using WEKA

Web-based social networks have become more prevalent as a medium for connecting like-minded people. The public accessibility of such social networks with the capability to share information, thoughts, opinions, and experience offers great potential to mankind and organizations. Social network has gained amazing attention in the last decade. Accessing social network sites such as Facebook, Google+, Twitter and LinkedIn through the internet and the web 2.0 technologies has become more affordable. Facebook is social networking service on which after registering on the the site, users can create their profile, add other users as friends, interchange messages, post status updates, photos, and share videos etc. People are more interested in and relying on Facebook for information, news and opinion of other users on various subject matters. Based on the data available for the facebook, the number of profiles has increasing expressively but with the fast growth of users, fake profiles/users have also grown. The WEKA data mining tool was used by performing adjustments of the attributes in order to come up with a decisive output. This paper presents the comprehensive review of social network and the trustworthiness of social networks

A Detailed Dominant Data Mining Approach for Predictive Modeling of Social Networking Data using WEKA

Social network has gained popularity manifold in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. In this Paper, we present the first comprehensive review of social and computer science literature on trust in social networks. We first review the existing definitions of trust and define social trust in the context of social networks. Web-based social networks have become popular as a medium for disseminating information and connecting like-minded people. The public accessibility of such networks with the ability to share opinions, thoughts, information, and experience offers great promise to enterprises and governments. As the popularity increases and they became widely used as one of the important sources of news, people become more cautious about determining the trustworthiness of the information which is disseminating through social media for various reasons. For this reason, knowing the factors that influence the trust in social media content became very important. In this research paper, we use a survey as a mechanism to study trust in social networks. First, we prepared a questionnaire which focuses on measuring the ways in which social network users determine whether content is true or not and then we analyzed the response of individuals who participated in the survey and discuss the results in a focus group session. Then, the responses, we get from the survey and the focus group was used as a dataset for modeling trust, which incorporates factors that alter trust determination. The dataset preprocessing a total of 56 records were used for building the models. This Paper applies the Decision Tree, Bayesian Classifiers and Neural Network predictive data mining techniques in significant social media factors for predicting trust. To accomplish this goal: The WEKA data mining tool is used to evaluate the J48, Naïve Bayes and Multilayer Perception algorithms with different experiments were made by performing adjustments of the attributes and using various numbers of attributes in order to come up with a purposeful output

A Detailed Dominant Data Mining Approach for Predictive Modeling of Social Networking Data using WEKA

Social network has gained popularity manifold in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. In this Paper, we present the first comprehensive review of social and computer science literature on trust in social networks. We first review the existing definitions of trust and define social trust in the context of social networks. Web-based social networks have become popular as a medium for disseminating information and connecting like-minded people. The public accessibility of such networks with the ability to share opinions, thoughts, information, and experience offers great promise to enterprises and governments. As the popularity increases and they became widely used as one of the important sources of news, people become more cautious about determining the trustworthiness of the information which is disseminating through social media for various reasons. For this reason, knowing the factors that influence the trust in social media content became very important. In this research paper, we use a survey as a mechanism to study trust in social networks. First, we prepared a questionnaire which focuses on measuring the ways in which social network users determine whether content is true or not and then we analyzed the response of individuals who participated in the survey and discuss the results in a focus group session. Then, the responses, we get from the survey and the focus group was used as a dataset for modeling trust, which incorporates factors that alter trust determination. The dataset preprocessing a total of 56 records were used for building the models. This Paper applies the Decision Tree, Bayesian Classifiers and Neural Network predictive data mining techniques in significant social media factors for predicting trust. To accomplish this goal: The WEKA data mining tool is used to evaluate the J48, Na�ve Bayes and Multilayer Perception algorithms with different experiments were made by performing adjustments of the attributes and using various numbers of attributes in order to come up with a purposeful output

Visual recognition of gestures in a meeting to detect when documents being talked about are missing

Meetings frequently involve discussion of documents and can be significantly affected if a document is absent. An agent system capable of spontaneously retrieving a document at the point it is needed would have to judge whether a meeting is talking about a particular document and whether that document is already present. We report the exploratory application of agent techniques for making these two judgements. To obtain examples from which an agent system can learn, we first conducted a study of participants making these judgements with video recordings of meetings. We then show that interactions between hands and paper documents in meetings can be used to recognise when a document being talked about is not to hand. The work demonstrates the potential for multimodal agent systems using these techniques to learn to perform specific, discourse-level tasks during meetings

Segmentación de mercado usando técnicas de minería de datos en redes sociales

Las redes sociales han ganado una gran popularidad durante la última década gracias al avance de nuevas tecnologías y al creciente interés de las personas por generar contenidos y compartirlos con sus contactos. Esto hace que los datos generados en las redes sociales crezcan exponencialmente con el tiempo. Estos datos generados contienen información que se puede ser analizada con el fin de descubrir patrones que ayuden en múltiples disciplinas. El marketing es una de estas disciplinas que está estrechamente ligada a entender comportamientos, tendencias o gustos de las personas El objetivo de este trabajo consiste en la aplicación de minería de datos (MD) para lograr el descubrimiento de patrones en datos provenientes de redes sociales. A partir de la obtención de patrones se busca realizar distintos tipos de segmentaciones que ayuden a los profesionales de marketing a orientar sus campañas.XV Workshop Bases de Datos y Minería de Datos (WBDDM)Red de Universidades con Carreras en Informática (RedUNCI

Segmentación de mercado usando técnicas de minería de datos en redes sociales

Las redes sociales han ganado una gran popularidad durante la última década gracias al avance de nuevas tecnologías y al creciente interés de las personas por generar contenidos y compartirlos con sus contactos. Esto hace que los datos generados en las redes sociales crezcan exponencialmente con el tiempo. Estos datos generados contienen información que se puede ser analizada con el fin de descubrir patrones que ayuden en múltiples disciplinas. El marketing es una de estas disciplinas que está estrechamente ligada a entender comportamientos, tendencias o gustos de las personas El objetivo de este trabajo consiste en la aplicación de minería de datos (MD) para lograr el descubrimiento de patrones en datos provenientes de redes sociales. A partir de la obtención de patrones se busca realizar distintos tipos de segmentaciones que ayuden a los profesionales de marketing a orientar sus campañas.XV Workshop Bases de Datos y Minería de Datos (WBDDM)Red de Universidades con Carreras en Informática (RedUNCI

Segmentación de mercado usando técnicas de minería de datos en redes sociales

Las redes sociales han ganado una gran popularidad durante la última década gracias al avance de nuevas tecnologías y al creciente interés de las personas por generar contenidos y compartirlos con sus contactos. Esto hace que los datos generados en las redes sociales crezcan exponencialmente con el tiempo. Estos datos generados contienen información que se puede ser analizada con el fin de descubrir patrones que ayuden en múltiples disciplinas. El marketing es una de estas disciplinas que está estrechamente ligada a entender comportamientos, tendencias o gustos de las personas El objetivo de este trabajo consiste en la aplicación de minería de datos (MD) para lograr el descubrimiento de patrones en datos provenientes de redes sociales. A partir de la obtención de patrones se busca realizar distintos tipos de segmentaciones que ayuden a los profesionales de marketing a orientar sus campañas.XV Workshop Bases de Datos y Minería de Datos (WBDDM)Red de Universidades con Carreras en Informática (RedUNCI

A survey of data mining techniques for social media analysis

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors

Web Person Name Disambiguation Using Social Links and Enriched Profile Information

In this article, we investigate the problem of cross-document person name disambiguation, which aimed at resolving ambiguities between person names and clustering web documents according to their association to different persons sharing the same name. The majority of previous work often formulated cross-document name disambiguation as a clustering problem. These methods employed various syntactic and semantic features either from the local corpus or distant knowledge bases to compute similarities between entities and group similar entities. However, these approaches show limitations regarding robustness and performance. We propose an unsupervised, graph-based name disambiguation approach to improve the performance and robustness of the state-of-the-art. Our approach exploits both local information extracted from the given corpus, and global information obtained from distant knowledge bases. We show the effectiveness of our approach by testing it on standard WePS datasets. The experimental results are encouraging and show that our proposed method outperforms several baseline methods and also its counterparts. The experiments show that our approach not only improves the performances, but also increases the robustness of name disambiguation