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.

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.

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.

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.

Big Data in Social Media Environment

2017 ◽  
pp. 70-93
Author(s):  
Matilda S.
Keyword(s):  
Social Media ◽  
Big Data ◽  
Driving Forces ◽  
Management Practice ◽  
Big Data Analytics ◽  
Business Leaders ◽  
The Social ◽  
Business Requirement ◽  
Intelligent Information ◽  
Segment Data

Information technology has reached its pinnacle, with the era being dominated by two hi-tech driving forces - Big data and Social media. Big data encompasses a wide array of data mining workloads, extracted through various sources, the results of which are of keen interest to business leaders and analysts across every industry segment. Data from the social media is exploding at an exponential rate and is being hailed as the key, to crucial insights into human behavior. Extracting intelligent information from such immense volume, variety and velocity of data, in context to the business requirement is the need of the hour. Therefore, new tools and methods specialized for big data analytics is crucial, along with the architectures for managing and processing such data. Big data complemented with Social Media offers a new horizon to take management practice to an advanced level.

scholarly journals A three-layer model on users’ interests mining

2017 ◽  
Vol 44 (1) ◽  
pp. 136-144 ◽  
Author(s):  
Renfeng Yang ◽  
Wenbo Xie ◽  
Duanbing Chen
Keyword(s):  
Social Media ◽  
Big Data ◽  
Layer Model ◽  
Short Term ◽  
Social Media Data ◽  
Sina Weibo ◽  
Hit Rate ◽  
The Social ◽  
Media Data

With the advent of big data era, social media plays an important role in many areas such as security and finance. Researchers pay more attention on mining users’ interests through the social media data. A three-layer model (TLM) based on keyword extracting is proposed to mine users’ interests, which includes candidate words extracting, semantic structures analysing and interest words ranking. The TLM mainly focuses on both self-importance and semantic-importance of interest words. In addition, the TLM also considers the interest drifting to track long-term and short-term interests of users. Experiments conducted on 10 SINA Weibo datasets show that TLM is more efficient than existing methods to identify users’ interests based on hit rate.

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