Prediction of sales using Big data analytics

Social media is a main source of collecting big-data. Data analysis converting their bigger data  to smart data. Smart data is acquired with the help of Apache Flume, Apache hive and Apache HDFS, smart data increase the sales of Marketing industry. It helps product owner to analyze people’s opinion about their product and consumer can analyze the reviews of  product before purchase. If tweets came along with Location, data analyzed based on the location

Predicting Rising Follower Counts on Twitter Using Profile Information

When evaluating the cause of one's popularity on Twitter, one thing is considered to be the main driver: Many tweets. There is debate about the kind of tweet one should publish, but little beyond tweets. Of particular interest is the information provided by each Twitter user's profile page. One of the features are the given names on those profiles. Studies on psychology and economics identified correlations of the first name to, e.g., one's school marks or chances of getting a job interview in the US. Therefore, we are interested in the influence of those profile information on the follower count. We addressed this question by analyzing the profiles of about 6 Million Twitter users. All profiles are separated into three groups: Users that have a first name, English words, or neither of both in their name field. The assumption is that names and words influence the discoverability of a user and subsequently his/her follower count. We propose a classifier that labels users who will increase their follower count within a month by applying different models based on the user's group. The classifiers are evaluated with the area under the receiver operator curve score and achieves a score above 0.800.Comment: 10 pages, 3 figures, 8 tables, WebSci '17, June 25--28, 2017, Troy, NY, US

Mining micro-influencers from social media posts

Micro-influencers have triggered the interest of commercial brands, public administrations, and other stakeholders because of their demonstrated capability of sensitizing people within their close reach. However, due to their lower visibility in social media platforms, they are challenging to be identified. This work proposes an approach to automatically detect micro-influencers and to highlight their personality traits and community values by computationally analyzing their writings. We introduce two learning methods to retrieve Five Factor Model and Basic Human Values scores. These scores are then used as feature vectors of a Support Vector Machines classifier. We define a set of rules to create a micro-influencer gold standard dataset of more than two million tweets and we compare our approach with three baseline classifiers. The experimental results favor recall meaning that the approach is inclusive in the identification

Political conversations on Twitter in a disruptive scenario: The role of "party evangelists" during the 2015 Spanish general elections

"This is an Accepted Manuscript of an article published by Taylor & Francis in The Communication Review on 2019, available online: https://www.tandfonline.com/doi/full/10.1080/10714421.2019.1599642"[EN] During election campaigns, candidates, parties, and media share their relevance on Twitter with a group of especially active users, aligned with a particular party. This paper introduces the profile of ¿party evangelists,¿ and explores the activity and effects these users had on the general political conversation during the 2015 Spanish general election. On that occasion, the electoral expectations were uncertain for the two major parties (PP and PSOE) because of the rise of two emerging parties that were disrupting the political status quo (Podemos and Ciudadanos). This was an ideal situation to assess the differences between the evangelists of established and emerging parties. The paper evaluates two aspects of the political conversation based on a corpus of 8.9 million tweets: the retweet- ing effectiveness, and the sentiment analysis of the overall conver- sation. We found that one of the emerging party¿s evangelists dominated message dissemination to a much greater extent.The present research was supported by the Ministerio de Economia y Competitividad [CSO2013-43960-R] [CSO2016-77331-C2-1-R]. The present research was supported by the Ministerio de Economia y Competitividad, Spain, under Grants CSO2013-43960-R ("2015-2016 Spanish political parties' online campaign strategies") and CSO2016-77331-C2-1-R ("Strategies, agendas and discourse in electoral cybercampaigns: media and citizens"). This work was possible thanks to help received from Emilio Giner in his task of extracting the corpus of tweets and from assistance provided by Mike Thelwall and David Vilares in the use of the SentiStrength application. 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A Survey on Data-Driven Evaluation of Competencies and Capabilities Across Multimedia Environments

The rapid evolution of technology directly impacts the skills and jobs needed in the next decade. Users can, intentionally or unintentionally, develop different skills by creating, interacting with, and consuming the content from online environments and portals where informal learning can emerge. These environments generate large amounts of data; therefore, big data can have a significant impact on education. Moreover, the educational landscape has been shifting from a focus on contents to a focus on competencies and capabilities that will prepare our society for an unknown future during the 21st century. Therefore, the main goal of this literature survey is to examine diverse technology-mediated environments that can generate rich data sets through the users’ interaction and where data can be used to explicitly or implicitly perform a data-driven evaluation of different competencies and capabilities. We thoroughly and comprehensively surveyed the state of the art to identify and analyse digital environments, the data they are producing and the capabilities they can measure and/or develop. Our survey revealed four key multimedia environments that include sites for content sharing & consumption, video games, online learning and social networks that fulfilled our goal. Moreover, different methods were used to measure a large array of diverse capabilities such as expertise, language proficiency and soft skills. Our results prove the potential of the data from diverse digital environments to support the development of lifelong and lifewide 21st-century capabilities for the future society