How do I participate in this course

As an integral part of society, people are creating and disseminating data every day, information are transmitted between entities, resulting in a rich social network effect. With regarding to understanding and using social networks, this course explains the definitions of social networks, information, and human cognition, as well as semantic analysis and social network analysis as the main content. Through with writing blogs, project assignments, and group projects to facilitate the understanding of knowledge. The learning process and online participation are as follows.

Blog

I wrote three blogs separately to explain the social media analysis I understand, SDGs of the United Nations and the application of social media analysis in SDGs of the United Nations. The most relevant part is social media analysis.

Some knowledge is involved in social media analysis.

Social network

The first part is to understand what a social network is. The social network is composed of entities and links between them. In the social network, human is the entity, and human cognition is also learned in the subsequent courses. Human can convert the vision, sound and memory information into abstract information and convey body instructions.

Sentiment analysis

The second part, the method of social media analysis. This part involves sentiment analysis and social network analysis. Most of the data collected from social networking sites is text, which requires text analysis, such as NLP, language models, lexicon based approach for sentiment analysis.

Social networks analysis

When doing social networks analysis, we will analyze multiple indicators, Sciomatrix, Density, Degree Centrality, Closeness Centrality and Betweenness Centrality.  These indicators are introduced to find out the mutual relationships in social networks.

Project assignments

In project assignments, the participation in the blog was evaluated from two perspectives. The first is to calculate the frequency of positive and negative vocabulary for the content of others’ comments for scoring. The second is to use a matrix that records blog comments to perform social network analysis on social networks formed by blog interactions and calculate multiple indicators. The result of my participation is shown in figure 1.

The in_degree is 8, which means 8 people have leave comments on my blogs, corresponding to it is that I have reviews to 9 people. Closeness and betweenness represent shortest path between two vertices and betweenness of a vertex represents the proportion of shortest paths between all other pairs that the actor in concern resides on. In these part I got 0.4284 and 0.0098, from which I can find I am trying to participant but not so popular in the social network.