Sensitivity Mining in Social Pulses to Address Cultural Heritage Competitive Intelligence

2016 ◽  
Vol 7 (2) ◽  
pp. 40-56 ◽  
Author(s):  
Angelo Chianese ◽  
Fiammeta Marulli ◽  
Francesco Piccialli
Keyword(s):  
Social Media ◽  
Big Data ◽  
Cultural Heritage ◽  
Diffusion Processes ◽  
Competitive Intelligence ◽  
Cultural Organizations ◽  
Multiple Dimensions ◽  
The Social ◽  
Social Data Mining ◽  
And Diffusion

The remarkable opportunities of discovering interesting knowledge from Big Data can be exploited in the Cultural Heritage (CH) domain, where Social Data Mining could advantage cultural organizations and operators with strategical elements for enjoying and attracting and enjoy visitors, as well as to support knowledge sharing and diffusion processes. A challenging and profitable direction may be combining Social Media Pulses Mining with Business Intelligence, to reveal the underlying key performance indicators, so leading to a ‘competitive intelligence' whose application well fits with CH. The main contribution of the proposed research is the application of a Multidimensional Text Mining over multiple dimensions for social media pulses analysis. A set of exploratory studies were performed on textual messages (Twitter) to explore multiple kind of relations between terms, their compliance with CH, and finally estimating the Social Sensitivity Indicator to this domain and its polarity. Advanced technologies for Big Data processing were exploited.

scholarly journals Semantic Analysis of Cultural Heritage News Propagation in Social Media: Assessing the Role of Media and Journalists in the Era of Big Data

2021 ◽  
Vol 13 (1) ◽  
pp. 341
Author(s):  
Theodora A. Maniou
Keyword(s):  
Social Media ◽  
Big Data ◽  
Cultural Heritage ◽  
Semantic Analysis ◽  
Audience Engagement ◽  
Media Environment ◽  
The Social ◽  
Social Media Platforms ◽  
Conflicting Role

In the era of big data, within the intense environment of social media, the effective communication of cultural heritage initiatives is considered of equal or—in some cases—even greater importance than heritage data themselves. Media and journalists play a critical and in some cases conflicting role in audience engagement and the sustainable promotion of cultural heritage narratives within the social media environment. The aim of this study was to assess the role of media and journalists in propagating cultural heritage news through social media platforms, and the narratives they tend to create in the digital public sphere. A qualitative approach is employed as a means of examining in-depth specific narratives, their meaning(s) and connotation(s), using semantic analysis.

scholarly journals Big Data Social Media Analytics for Purchasing Behaviour

2018 ◽  
Vol 7 (4.36) ◽  
pp. 463
Author(s):  
Shahid Shayaa ◽  
Ainin Sulaiman ◽  
Arsalan Zahid Piprani ◽  
Mohammed Ali Al-Garadi ◽  
Muhammad Ashraf
Keyword(s):  
Social Media ◽  
Big Data ◽  
Social Media Analytics ◽  
Purchasing Behavior ◽  
The Social ◽  
Purchasing Behaviour

The social media is rich in data and of late its data have been used for various types of analytics. This paper examines the purchasing behavior and sentiments of social media users from Jan - 2015 to Dec – 2016. The purchasing behaviour of the users is categorized into five: buy car, buy house, buy computer, buy hand phone and going for holiday. The paper will also demonstrate the trend of each individual category. The results of the analysis would provide businesses information on the social media users’ purchasing behavior, their sentiment thus allowing them to take more appropriate strategies to enhance their competitiveness.  

scholarly journals Human Behavior Analysis Using Intelligent Big Data Analytics

2021 ◽  
Vol 12 ◽  
Author(s):  
Muhammad Usman Tariq ◽  
Muhammad Babar ◽  
Marc Poulin ◽  
Akmal Saeed Khattak ◽  
Mohammad Dahman Alshehri ◽  
...  
Keyword(s):  
Social Media ◽  
Big Data ◽  
Human Behavior ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Apache Spark ◽  
Social Media Data ◽  
The Social ◽  
Media Data

Intelligent big data analysis is an evolving pattern in the age of big data science and artificial intelligence (AI). Analysis of organized data has been very successful, but analyzing human behavior using social media data becomes challenging. The social media data comprises a vast and unstructured format of data sources that can include likes, comments, tweets, shares, and views. Data analytics of social media data became a challenging task for companies, such as Dailymotion, that have billions of daily users and vast numbers of comments, likes, and views. Social media data is created in a significant amount and at a tremendous pace. There is a very high volume to store, sort, process, and carefully study the data for making possible decisions. This article proposes an architecture using a big data analytics mechanism to efficiently and logically process the huge social media datasets. The proposed architecture is composed of three layers. The main objective of the project is to demonstrate Apache Spark parallel processing and distributed framework technologies with other storage and processing mechanisms. The social media data generated from Dailymotion is used in this article to demonstrate the benefits of this architecture. The project utilized the application programming interface (API) of Dailymotion, allowing it to incorporate functions suitable to fetch and view information. The API key is generated to fetch information of public channel data in the form of text files. Hive storage machinist is utilized with Apache Spark for efficient data processing. The effectiveness of the proposed architecture is also highlighted.

Creating Value through Social Media: Fresh Evidence from Cultural Organizations

2018 ◽  
Vol 4 (2) ◽  
pp. 243-254 ◽  
Author(s):  
Cristina Caterina Amitrano ◽  
Roberta Gargiulo ◽  
Francesco Bifulco
Keyword(s):  
Social Media ◽  
Cultural Heritage ◽  
Digital Technologies ◽  
Service Marketing ◽  
Customer Engagement ◽  
Communication Tools ◽  
Cultural Organizations ◽  
Potential Customers

The impacts of digital technologies are gaining increasing attention in the service literature, and a growing number of cultural organizations are using online websites and social media to interact with their actual and potential customers. However, the contributions developed by service marketing scholars show little interest in examining the role of underlying technologies in a particular service experience context, namely, the cultural heritage context and the corresponding visiting experience. Therefore, the purpose of this research is to analyse how digital technologies, especially social media, can help cultural organizations stimulate customer engagement. To reach this aim, we conducted a single exploratory case study of a communication project developed by the National Archaeological Museum of Naples (MANN) to attract their actual and potential Italian and foreign visitors. The achieved results allow for us to show how digital communication tools can stimulate customer engagement in a cultural heritage context.

Fake News Finds an Audience

2019 ◽  
pp. 201-222
Author(s):  
Erik P. Bucy ◽  
John E. Newhagen
Keyword(s):  
Social Media ◽  
Big Data ◽  
Information Processing ◽  
Educational Strategy ◽  
Fake News ◽  
False Information ◽  
User Experiences ◽  
The Social ◽  
Social Media Platforms ◽  
Media Systems

The vulnerabilities shown by media systems and individual users exposed to attacks on truth from fake news and computational propaganda in recent years should be considered in light of the characteristics and concerns surrounding big data, especially the volume and velocity of messages delivered over social media platforms that tax the average user’s capacity to determine their truth value in real time. For reasons explained by the psychology of information processing, a high percentage of fake news that reaches audiences is accepted as true, particularly when distractions and interruptions typify user experiences with technology. As explained in this essay, fake news thrives in environments lacking editorial policing and epistemological vigilance, making the social media milieu ideally suited for spreading false information. In response, we suggest the value of an educational strategy to combat the dilemma that digital disinformation poses to informed citizenship.

scholarly journals La foto social: de la nostalgia al big data

2020 ◽  
Vol 14 (1) ◽  
pp. 109-110
Author(s):  
Jaime López Díez
Keyword(s):  
Social Media ◽  
Big Data ◽  
The Social

Reseña de la obra Jurgenson, N. (2019). The Social Photo. On Photography and Social Media. Nueva York: Verso.

Generating Big Data

2019 ◽  
pp. 136-161
Author(s):  
Ezer Osei Yeboah-Boateng
Keyword(s):  
Social Media ◽  
Big Data ◽  
Business Processes ◽  
Data Types ◽  
Online Data ◽  
Data Formats ◽  
Best Value ◽  
The Social ◽  
Sustainable Value ◽  
The Right

Big data is characterized as huge datasets generated at a fast rate, in unstructured, semi-structured, and structured data formats, with inconsistencies and disparate data types and sources. The challenge is having the right tools to process large datasets in an acceptable timeframe and within reasonable cost range. So, how can social media big datasets be harnessed for best value decision making? The approach adopted was site scraping to collect online data from social media and other websites. The datasets have been harnessed to provide better understanding of customers' needs and preferences. It's applied to design targeted campaigns, to optimize business processes, and to improve performance. Using the social media facts and rules, a multivariate value creation decision model was built to assist executives to create value based on improved “knowledge” in a hindsight-foresight-insight continuum about their operations and initiatives and to make informed decisions. The authors also demonstrated use cases of insights computed as equations that could be leveraged to create sustainable value.

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