media update’s Aisling McCarthy answers three of the most poignant questions about the relationship between AI and social media tracking.

Question 1: “What is social media tracking?”

Social media tracking is a process that gathers information from social media platforms and analyses the data to gain insights.

It is made up of a combination of social media monitoring (which gathers up the raw data), social listening (which understands what consumers are saying about a brand) and social media analysis (which looks at the data to find patterns and insights).

The process results in actionable insights that can be used to boost marketing and business strategies. These insights can also give businesses a deeper understanding of the behaviour of their social media audiences and their preferences, which can inform their decision-making.

Question 2: “What kind of AI technology is used in social media tracking services?”

Social media tracking services, like amaSocial, use a combination of three AI-powered technologies. The technologies amaSocial use are:

1. Natural Language Processing

A component of machine learning that processes, sorts and categorises various elements of text.

While a human being can inherently understand the meaning of sentences and paragraphs, a computer cannot really understand language. Using the mechanics of language, computers can, however, identify the context and grammatical use of words in sentences.

2. Machine Learning

A process in which machines are fed data, and continually develop the algorithms they have been programmed with. The more data the machines are fed, the more accurately it can process similar information in future, and the more sophisticated the algorithms will become.

The data that the machine learns from needs to be consistent and accurate so that learning can take place as efficiently as possible.

3. Entity Extraction

A component of machine learning, which is used to identify different objects within text.

It can be used to quickly extract which people, places, brands and products are being referred to within a media clip. It is a very valuable media intelligence tool, which can assist in compiling reports, and strategy planning.

Entities within a text are the people, companies, products, and concepts referred to within a particular text. The process of entity extraction uses machine-learning algorithms, which are trained to automatically find the names of people, places, organisations, and products.

For example, in the sentence, “John began work at Coca-Cola in 2010”, a machine learning algorithm would identify it as follows: John [Person] began work at Coca-Cola [Organisation] in 2010 [Time].

Question 3: “How does AI make social media tracking and reporting better?”

The inclusion of AI-powered technology into social media tracking and reporting services allows them to offer a more holistic service. With so much data available, AI technologies ensure that these service providers can keep up with the masses of Big Data.

Utilising AI technology, like Natural Language Processing (NLP), can make finding brand mentions more accurate. NLP systems allow computers to understand the context of a social media user’s post. This is a very useful tool for social media monitoring tasks.

NLP allows irrelevant posts to be filtered out of the results, only showing teams the posts that mention their brands. The technology can interpret the context of the post and identify when users are not mentioning the brand, but are referring to a wholly different topic – a feat hard to achieve without the use of AI.

When it comes to social listening, machine learning and entity extraction can help to better understand the topics that consumers are talking about. Entity extraction can assist brands in finding talked-about topics in a more efficient way than searching for keywords.

Finally, sentiment analysis, which is done automatically with machine learning, can help to show businesses the reaction of social media users to a particular brand.

In a 2017 interview with media update, Anja van Schalkwyk, senior strategic analyst at Focal Points, said that sentiment analysis relies on both NLP and machine learning.

“This combination allows the engine we use to rapidly analyse the sentiment of media clips with near-100% accuracy. This automatic analysis process has freed up time for our team to mine deeper insights through qualitative analysis.”

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Social media tracking is made up of various elements, all vital to keeping an eye on your brand. Find out how to use one of the elements in our article, BLOG: Three ways your brand can use social media monitoring.