Chances and Challenges of Computational Data Gathering and Analysis

Abstract

Words count: Core text (Title-References) 7677, Tables and Figure captions 1099. Digital and social media and large available data sets generate various new possibilities and challenges for doing research focused on perpetually developing online news ecosystems. This paper presents a novel computational technique for gathering and processing large quantities of data from Facebook. We demonstrate how to use this technique for detecting and analyzing issue-attention cycles and news flows in Facebook groups and pages. Although the paper concentrates on a Finnish Facebook group as a case study, the method demonstrated can be used for gathering and analyzing large sets of data from various social network sites and national contexts. The paper also discusses Facebook platform regulations of data gathering and ethical issues of doing online research.

Biological, Chemical, and Nutritional Food Risks and Food Safety Issues From Italian Online Information Sources: Web Monitoring, Content Analysis, and Data Visualization

“…To answer RQ2, we first performed an automated content analysis. Automatic content analysis procedures based on text mining techniques have gained importance and popularity in the digital media environment due to the presence of larger datasets [ 34 - 36 ], and these methods have already successfully been used to analyze text that refers to food risks and safety issues [ 37 - 40 ]. A subcorpus composed of all validated content without content published on social media was extracted.…”

Participation in Social Media: Studying Explicit and Implicit Forms of Participation in Communicative Social Networks

“…Social media and the Internet in general generate a need for new forms of data analysis and software-supported data capture (Rieder, Abdulla, Poell, Woltering, & Zack, 2015), such as network analysis. For example, Facebook produces diverse, broad data, which can be gathered using computational techniques (Sormanen et al, 2016), such as an application programming interface (API). API-based data can highlight the role of administrators as connective leaders, measure the size and composition of the participating audience, engage in various types of periodisation and investigate issues that have been raised in comment sections (Rieder et al, 2015).…”

Biological, Chemical, and Nutritional Food Risks and Food Safety Issues From Italian Online Information Sources: Web Monitoring, Content Analysis, and Data Visualization (Preprint)

“…To answer RQ2, we first performed an automated content analysis. Automatic content analysis procedures based on text mining techniques have gained importance and popularity in the digital media environment due to the presence of larger datasets [34][35][36], and these methods have already successfully been used to analyze text that refers to food risks and safety issues [37][38][39][40]. A subcorpus composed of all validated content without content published on social media was extracted.…”