social network security

• www.ghostery.com

Ghostery looks for third party page elements (which we call "3pes") on the web pages you visit.
These can be things like social network plugins, advertisements, invisible pixels used for tracking and analytics, etc.
Ghostery notifies you that these things are present, and which companies operate them.
You can learn more about these companies, and if you wish, choose to block the 3pes they operate.

• disconnect.me

nce installed on your Web browser, these extensions will tell you how many trackers they have blocked

• www.secure.me

24/7 check of all posts
Protection from dangerous links and viruses
Monitoring of all photos, friends and activities

• privacyfix.com

check your privacy settings across Facebook, Google and the other websites and companies collecting your data. Get to the fix with one click. Know when policies change.

• simplewa.sh

It is called Simplewash, formerly Facewash, and it looks for profanity, references to drugs and other faux pas that you do not necessarily want, say, a law school admissions officer to se

• socioclean.com

Socioclean is another application that scours your Facebook posts.

• Social Network Analysis

Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. The nodes in the network are the people and groups while the links show relationships or flows between the nodes. SNA provides both a visual and a mathematical analysis of human relationships. Management consultants use this methodology with their business clients and call it Organizational Network Analysis [ONA].

http://www.orgnet.com/sna.html

• Sentiment analysis uses

Sentiment analysis is extremely useful in social media monitoring as it allows us to gain an overview of the wider public opinion behind certain topics. Social media monitoring tools like Brandwatch Analytics make that process quicker and easier than ever before, thanks to real-time monitoring capabilities.
https://www.brandwatch.com/blog/understanding-sentiment-analysis/

• Powerful social listening

Add context to the billions of conversations happening online every day. Brandwatch Analytics tells you more about the opinions, trends and people impacting your business.
https://www.brandwatch.com/brandwatch-analytics/?utm_expid=69193390-33.eKwp4nn8QDGu7MAr35s0Sg.0&utm_referrer=https%3A%2F%2Fwww.brandwatch.com%2Fblog%2Funderstanding-sentiment-analysis%2F

• Sentiment analysis (sometimes known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media

https://en.wikipedia.org/wiki/Sentiment_analysis

• Basic Sentiment Analysis with Python

you can check out the code on Github
http://fjavieralba.com/basic-sentiment-analysis-with-python.html

• This tutorial steps through a Natural Language API application using Python code. The purpose here is not to explain the Python client libraries, but to explain how to make calls to the Natural Language API. Applications in Java and Node.js are essentially similar. Consult the Natural Language API Samples for samples in other languages (including this sample within the tutorial).

https://cloud.google.com/natural-language/docs/sentiment-tutorial

• Introduction to Sentiment Analysis Algorithms

Sentiment Analysis is the use of natural language processing, statistics, and text analysis to extract, and identify the sentiment of text into positive, negative, or neutral categories.
http://blog.algorithmia.com/introduction-sentiment-analysis-algorithms/

• Sentiment analysis is also called opinion mining since it includes identifying consumer attitudes, emotions, and opinions of a company’s product, brand, or service.Sentiment Analysis is the use of natural language processing, statistics, and text analysis to extract, and identify the sentiment of text into positive, negative, or neutral categories.

http://blog.algorithmia.com/introduction-sentiment-analysis-algorithms/

• Sentiment analytics involves the analysis of comments or words made by individuals to quantify the thoughts or feelings intended to be conveyed by words. Basically, it’s an attempt to understand the positive or negative feelings individuals have toward a brand, company, individual, or any other entity. In our experience, most of the sentiment collected around topics tends to be “neutral” (or convey no positive or negative feelings or meanings). It’s easiest to think about sentiment analytics when we look at Twitter data (or any other social site where people express a single thought or make a single statement). We can compute the sentiment of a document (such as a wiki post or blog entry) by looking at the overall scoring of sentiment words that it contains. For example, if a document contains 2,000 words that are considered negative versus 300 words that are considered positive in meaning, we may choose to classify that document as overall negative in sentiment. If the numbers are closer together (say 3,000 negative words versus 2,700 positive words or an almost equal distribution), we may choose to say that document is neutral in sentiment .The sentiment analysis being done by software is usually based on a sentiment dictionary for that language. The basic package comes with a predefined list of words that are considered as positive. Similarly, there is also a long list of words that can be considered negative. For many projects, the standard dictionary can be utilized for determining sentiment. In some special cases, you may have to modify the dictionary to include domain-specific positive and negative words. For example, the word Disaster can be a negative sentiment word in a majority of contexts, except when it is used to refer to a category of system such as “Disaster Recovery Systems.”

http://social-media-strategy-template.blogspot.com.tr/2016/04/sentiment-analysis-basics.html

• sentiment analysis is the attempt to derive the emotion or 'feeling' of a body of text. The field of sentiment analysis and opinion mining usually also involves some form of data mining to get the text.

http://sentdex.com/sentiment-analysis/

• Mining Twitter Data with Python (and JS) – Part 7: Geolocation and Interactive Maps

https://marcobonzanini.com/2015/06/16/mining-twitter-data-with-python-and-js-part-7-geolocation-and-interactive-maps/

• The Social-Engineer Toolkit (SET) was created and written by the founder of TrustedSec. It is an open-source Python-driven tool aimed at penetration testing around Social-Engineering

https://www.trustedsec.com/social-engineer-toolkit/ 

• What Is Website Cloaking?

A simple definition of the word cloaking is to conceal, hide or cause to be invisible. In the world of web design cloaking refers to showing different page content to regular users than what is shown to Google bot or other search engine crawlers.
Why is cloaking bad? Cloaking is frowned upon because it is a high risk violation of quality guidelines and accepted best practices for web page design. It also provides a level of deception as well as a bad user experience. The bad user experience is due to the search engine results not matching the actual page content. Also, in some cases cloaking is a method for transmitting malicious code to unsuspecting users.

https://impactsocialmedia.net/what-is-website-cloaking/