How to Read Network Map !

Network graphs are used to visualise the most frequent word associations within textual articles. These are useful to understand the context of a particular conversation and how it maps out. The network graphs for the dashboard are constructed by analysing the text of the multiple news articles that have been collected.

An interpretation that flows from the graphs is the degree of interconnectedness of keywords. Words or, in technical terms, nodes, which are highly associated with other words, will create groups or clusters within the graph. Thus these words will define a sub topic or a conversation within the news articles. Words which are not associated with each other will appear on different sides of the graph and will not be linked.

The network graphs also display the importance of a particular keyword within the context of the conversation. These are words which are most frequently associated with other words, and will thus mostly appear in the centre of the graph. Trying to move these central nodes will shift the entire graph in the direction of the node. On the other hand, words which are not as significant, and are usually in the periphery of the graph, will not be able to move the graph as much.

Introduction

The Adolescent Sexual Reproductive Health and Rights News Dashboard is devised with a purpose of identifying the media coverage on sexual and reproductive health and rights of adolescents within the Nepalese media. While the primary focus of the dashboard is to showcase news coverage of the different regions of Nepal it is however not geographically limited to Nepal. Articles in Nepalese newspapers reporting foreign events are also incorporated into the dashboard. The ASRHR News Dashboard is an interactive data visualization tool which contains a structured dataset of news reports and articles collected over a stipulated period of time designed to be accessed by all users including policy makers and citizens.

Methodology

CPC Analytics used TeslarĀ©, our proprietary news and public relations (PR) platform. Teslar automatically tracks nearly fourteen Nepalese newspapers spread over a period of six months and collects the required data. It also utilises advanced textual analysis of the newspaper articles to identify and cluster key textual patterns like occurrence and frequency of keywords. Following the analysis, it helped identify the five unique news sections, and assign an appropriate section to each based on the content and purpose. With an aim of creating a visual representation of the insights gathered, a web based tool was used to construct an interactive online dashboard displaying the frequency of media reports in the Nepalese newspapers.