What And How Social Data

Kling: Explain the purpose of social informatics and how researchers could use Kling's "orientations" and other concepts to study your digital life, or any place/thing/activity in the world of digital/online/information stuff?

Brian V. Carolan. (2014). 1: The Social Network Perspective and Educational Research Introduction. In Social Network Analysis and Education: Theory, Methods & Applications. (pp. 3-23). Thousand Oaks, CA: SAGE Publications, Inc. doi: http://dx.doi.org.library.gcu.edu:2048/10.4135/9781452270104.n1

The film ‘How Social Networks Predict Epidemics’ by Nicholas Christakis explores the influence of social networks on people’s lives and how it can be used to predict epidemics.Social networks are key connectors among individuals in all societies today. They are avenues through which people learn and share many new things with others. Social networks are so instrumental in determining many issues like employment and salaries, and the transmission of diseases. It is, therefore, crucial to comprehend why and how the networks influence the patterns as they do.

Social media has emerged as the most powerful form of communication these days. The ability to voice an opinion from anywhere over anything has coined and popularized the term ‘Global Citizen. Many social platforms available these days provide the avenues for the companies and organization to make a brand for themselves by directly engaging with their customers. If you don’t like some brand you bought or a movie you just watched, login on Twitter or Facebook and tell the world. These platforms provide an access into what is currently trending or making the stir at the moment. Thus, there is a huge repository of opinionated data that if harnessed strategically can help to solve problems ranging from what product to invest into complex business

In 1992 Joseph Walther developed the idea of social information processing which is an interpersonal communication theory that describes how individuals establish relationships with one another through a computer-mediated environment and how they can progress and manage the relationships while using an online medium or channel (Van Der Heide, & Walther, 2009). Social information processing theory can be applied to various forms of media such as computers, phones, videogames, and television. The uses of these applications have different purposes. For instance online

The complexity of the relationships includes the amount of one way, two way, or more complicated relationships within the group. The diversity of people within the network is important because it helps distinguish is members are male, female, or if the members of the group are family. The subgroups, cliques, and linkages show the relationship between the members and includes whether they are members of another group. Finally the communication in the network is the flow of information from one member to the next and includes whether or not the one member is more credible than the next. The Social Network theory is a complex “sociogram” explaining the different relationships people can have and the impact they have on behavioral changes (Edberg,

In social network analysis, people within a network are referred to as nodes, and the relationships between people are referred to as edges.7 Each pair of nodes (i, j) is either connected or disconnected, that is, the contact direction is not taken into account in the analysis of the influenza transmission. In the simplest data generation, the connections are defined as

In social network analysis, people within a network are referred to as nodes, and the relationships between people are referred to as edges.7 Each pair of nodes (i, j) is either connected or disconnected, that is, the contact direction is not taken into account in the analysis for the influenza transmission. In the simplest data generation, the connections are defined as

In recent years the amount of data accumulated from social networks has become very large, and there is a lot of valuable information to gain from analyzing and applying data mining to social network data.

Sentiment analysis concentrates on attitudes, whereas the traditional method of text mining mainly focuses on the analysis of facts. There are few main fields of research in Sentiment analysis: sentiment classification, feature based sentiment classification and opinion summarization. Sentiment classification deals with classifying entire document according to the opinions shown with respect to a certain object. While feature-based sentiment classification considers the opinions on the features of a certain object. Opinion summarization task is different compared to traditional text summarization because only the features of the product are examined on which the customers have expressed their opinions via any social media. Opinion summarization does not summarize the reviews by selecting a subset or rewriting some of the original sentences from the reviews to capture the main points like in the classic text summarization

Infectious and deadly diseases have been known to spread over social networks of people and animals. Network epidemiology has been proved as an indispensable approach for understanding epidemics of infectious diseases and is often used in medical epidemiology and network science. The most famous and practical example of network epidemiology is the GLEAMviz platform, which succeeded in forecasting the 2009 H1N1 pandemic, saving millions of lives. Behind network epidemiology, one can assume that there is a link (i.e. contact) between two or more individuals is a way for contagion. This is a strong simplification of reality as there is already data processing temporal information of the event of a contagious disease between humans and other animals. SocioPatterns is one of the many data processors that collects and analyzes data.

The complete dataset consists of approximately 19 million Facebook status updates written by 136,000 participants. The scientists used three steps to analyze the data: the first step is linguistic feature extraction (a- word or phrase, b- topic), the second step is correlational analysis which means the correlation of the linguistic feature with the gender, age and personality. The

Abstract- This survey reviews the recent progress in the field of sentiment analysis with the focus on available datasets and sentiment analysis techniques. Since many exhaustive surveys on sentiment analysis of text input are available, this survey briefly summarizes text analysis techniques and focuses on the analysis of audio, video and multimodal input. This survey also describes different available datasets. In most of the work datasets are prepared as per specific research requirements. This survey also discusses methods used to prepare such datasets. This survey will be helpful for beginners to obtain an overview of available datasets, methods to prepare datasets sentiment analysis techniques, and challenges in this area.

The widespread use of social media sites such as Facebook, Instagram, Twitter and LinkedIn have been generating large amounts of data which are interchangeably known as social data. This data is usually user-generated, inconsistent, and incomplete and have geo information. This data can be used by researchers or companies to extract meaningful information about human behavior, product consumption, trends in markets, etc. Extraction and study of this data for useful purposes is called data mining of social media. Since the number of techniques used to mine data are increasing rapidly with time, it has become imperative to represent the data in a form which can be easily understood. Data mining may consist of the following stages. First, data is extracted,

Social networks with complex structure and large scale emerged with the development of social network sites. Various network communities gradually form complex structural pattern in the production and living of people. The competitive advantages and community distribution in networks can be obtained through analyzing the structure of community. Therefore the research key point of the current data mining field is how to find out the potential structure of such large-scale social networks. Currently, most of real networks have overlapping communities. All the users can be allocated to different communities according to different allocation rules. But the complex structure of network and mass node information are difficulties for mining