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When you scroll through your social media feed, you鈥檙e scrolling through a curated list of content recommendations offered to you by a machine learning (ML) algorithm. This algorithm has observed your online movements, identified patterns, and made predictions about what you might like to see when you log in. This process uses machine learning, or the program鈥檚 ability to analyze and learn from your behavior, with the goal of keeping you engaged on the platform as long as possible.聽

Beyond the algorithm, you can see machine learning in social media examples in a variety of places. Or, in the case of content moderation, it鈥檚 the content you don鈥檛 see that serves as an example of what machine learning can do. By observing social media users鈥� language, a content moderation algorithm can detect potentially harmful or dangerous words and prevent that content from spreading.聽

Many professionals who work with social media can benefit from machine learning algorithms in different ways. Web developers can use machine learning to create more engaging and safer spaces online. Business intelligence and marketing professionals can use social media to better understand their audience and create targeted campaigns or generate leads. Data scientists and other researchers can use social media to gain deeper insights into societal issues, such as how extremist views move through a population or how a specific illness or contagion spreads.聽

Explore examples of machine learning in social media, the benefits of AI in social media, and examples of how data scientists can use machine learning for social research to answer questions about people at a population level.聽

How is machine learning used in social media?

You can find machine learning in social media in many different places, including in the content the algorithm uses to populate your feed, how the algorithm knows who might like the content you share, and how it approaches moderating potentially dangerous or misleading content. Explore some of the main ways that you might see machine learning at work in social media platforms.聽

To recommend content you might like

One of the places where you can find machine learning in social media is in the algorithms that serve you content you enjoy and want to see more of. By analyzing your user behavior, such as the links you click on, the videos you watch or scroll by, or the kinds of content that make you stop and write a comment, the algorithm can learn what you find engaging and find similar content you may also enjoy. Some connections may seem obvious. For instance, when you react to a video about cute kittens, the algorithm serves you more cute kittens. However, social media machine learning algorithms can learn deep relationships between topics that aren鈥檛 immediately obvious to make predictions about other topics you will like.聽

To label and understand user-generated content

Machine learning can also power image classification systems that identify what objects are present in images, videos, and other content that users add to social media. This helps the algorithm identify what posts are about. Then, it can determine which users may want to see the content.聽

To filter out spam and moderate content

The same ability to classify the content of images and video can also identify spam, misleading content, and other content in need of moderation. Instead of analyzing the content of images, machine learning algorithms can understand the sentiment behind the words that people write on social media. Developers can then use that information to moderate speech that goes against community guidelines, and marketing professionals can use it as insight to guide their marketing and brand messaging efforts. Machine learning can also evaluate social media data to measure not only the sentiment about products but public opinion on events in the news or other topics that affect many people, such as politics.聽

To understand social media intelligence

Machine learning can also help marketing and business intelligence professionals understand the data users generate on social media and translate that information into actionable insights. In fact, every way that you can use machine learning in social media works because of an ML algorithm鈥檚 ability to understand and make predictions about data.聽

Business intelligence professionals and marketing researchers can use machine learning to understand relationships between points within the data, such as how users behave when presented with one tagline or another, and gain insight into how they can optimize their companies鈥� social media campaigns to best meet their goals. Marketing professionals can also use generative AI to translate the insights gathered from machine learning algorithms into generated content, such as images or marketing text, or to develop new ideas for marketing campaigns based on generative AI.聽

Benefits of AI in social media

Machine learning in social media offers many benefits, both for marketing professionals and social media users. For example, machine learning can understand customer behavior and predict what the customer likes and dislikes. Marketing professionals and other business analysts can use this data to create products, advertising, or experiences that customers enjoy and want. This leads to happier customers who purchase more items they want and need, allowing companies to earn more money and to build more loyal customers. Other benefits of AI and machine learning in social media include:聽

• Increased efficiency in content creation: AI can help marketing professionals generate the text and images they need for marketing campaigns.

• Social listening: Brand managers can monitor how people discuss their brand online to identify working strategies and redirect or resolve public relations (PR) challenges.聽

• Data insights: The amount of data users generate on social media feeds, coupled with machine learning algorithms for data analysis, can offer you a wealth of business intelligence, such as what your customers like and what they find engaging.聽

Challenges of machine learning in social media

In light of the many benefits that machine learning can provide for social media users and creators, it鈥檚 also important to consider some of the challenges that come with it.聽

Social media algorithms have introduced larger ethical topics into our public discourse. For example, 54 percent of Americans learn about at least some of the news and current events of the day on social media (25 percent of Americans report they 鈥渙ften鈥� learn about news on social media) []. Some thought leaders have raised questions about whether the social media algorithm could then have a much larger impact on public discourse if the algorithm is determining what news 54 percent of Americans are seeing or hearing about.聽

Machine learning algorithms have also been shown to develop biases, which could potentially occur when the model uses metrics such as skin color or gender to determine what content creators you see. The answer to bias in machine learning could potentially be another machine learning algorithm designed to detect bias in algorithm results. As researchers continue to develop machine learning for social media and other uses in society, these are a few of the challenges they will try to overcome.聽

How machine learning and social media can provide other social insights

In addition to using machine learning to make social media more engaging and to understand social media analytics better, you can also use machine learning algorithms and data from social media to gain deeper insight into society. For example, health care researchers can use social media data to monitor a population for signs that a contagious disease may be spreading. This may include looking for posts on social media that indicate users have symptoms of an illness or noticing when users start to search for information about an illness or symptoms on a search engine. This kind of data analysis could allow researchers to notify the public when a contagious illness is spreading through the community and how to mitigate infection.聽

Monitoring the population via social media can also inform researchers about the need for other public health initiatives or help them determine the best way to distribute humanitarian aid. This might involve either 鈥渓istening in鈥� on populations to determine need or using social media messages in the wake of a natural disaster or other crisis.聽

Social media can also help researchers understand shifts in public opinion, such as how different demographics shift along political ideology and, potentially, why these shifts occur. This can help identify the spread of extremist ideas or disinformation campaigns designed to mislead the public.

Learn more about machine learning on 糖心vlog官网观看

Machine learning in social media can help users find engaging content, marketing professionals design campaigns that hit their mark, business intelligence analysts unlock data insights, and social scientists understand the greater needs of a population. If you want to learn more about how to use machine learning in social media or other use cases, you can find programs to help you learn more on 糖心vlog官网观看. For example, you can explore the Machine Learning Specialization offered by Stanford Deep Learning.AI for the chance to learn how to apply best practices for machine learning development and use unsupervised learning techniques.

Interested in how companies use social media in marketing? With the Meta Social Media Marketing Professional Certificate, you鈥檒l explore how to develop effective social media posts and create a strong social media brand presence, including the use of AI tools.