Artificial Intelligence in Social Media
By using AI tools, marketers can choose appropriate social strategies, track audience behavior, and analyze marketing performance, allowing you to focus more on the more creative aspects of marketing operations. In this article, we’ll walk you through what you need to know about AI and how to use it to optimize your social media marketing strategy.
Intelligent filtering and recommendation of content
Using big data to describe user portraits is almost based on user volume Billions of social media platforms are stepping into artificial intelligence. When social media first emerged, friends and subscriptions were almost the only driving force. Users relied on timelines to obtain information about their subscribers and friends.
A few years ago, Twitter and Weibo successively diluted the concept of timeline through information flow optimization. Later, Facebook also used information flow to optimize social media. The biggest difference between the information flow and the traditional timeline is that content presentation is organized based on relevance and interest. What users can see, what they see first, and what they see later are all determined by the algorithm in the black box.
Based on its hundreds of millions of users, hundreds of billions of people, and nearly one trillion pieces of content, Weibo depicts a map of Chinese netizens; the data types are rich and the content is extensive. In terms of content distribution, direct recommendations are made based on relationship flows and interest flows; in content production, high-quality content is mined, automatic titles and summaries are provided, etc.
Assist marketing analysis to improve efficiency
The magical wizard Steve Wozniak, who co-founded Apple Computer with Steve Jobs, was asked about his dream job When asked what the final product would be, he replied that he hoped it would be something that "gives him more time." We live in an era where a delay of just 400 milliseconds in a Google search results in 8 million fewer searches, and the speed of insight needs to increase dramatically.
Companies such as Brandwatch, which provides social media monitoring services, are trying to use artificial intelligence to reduce the time social analysts spend searching for brand data. Compared with the average 3.2 hours spent per week in the past, social analysts can now focus on more important things, and AI makes relevant data easier to interpret and more accessible to the entire organization.
Brandwatch pulls together heterogeneous data by analyzing the peaks and troughs in a chart, and then uses it to explain why a certain chart peaked at a particular moment—perhaps an article Social media articles align with news events from the same industry, attracting new audiences to the channel. These AI insights make social media marketing analytics reporting more straightforward because they take the guesswork out of social analytics.
Improve user experience
People like to do business with brands that provide quality services. By integrating artificial intelligence into social media, audience preferences will be better understood. Artificial intelligence can create content, target ads and improve products or services to enhance user experience. It can quickly identify problem areas and fix them immediately, respond to user problem complaints promptly, and provide the best customer experience.
Competitor Analysis
If you want to stay ahead of the competition, you must understand the competition to find out the corresponding methods. AI-based analytics can accurately and quickly analyze your competitors’ social profiles. Track their reach, engagement rates, conversion rates, how customers perceive them and the effective strategies they employ. Armed with this information, you can optimize your social strategy to increase engagement and increase conversions.
Collect audience perspectives
Artificial intelligence helps integrate tools like social listening that can analyze social media posts at scale Articles, listen to what people are saying about your brand and discover emerging trends or new target audiences.
AI-generated consumer opinions will solidify connections with audiences and increase brand reputation and asset value. People may use your products and services in unanticipated ways, and understanding these perspectives will open up new avenues for brand promotion.
There is no doubt that with the increasing development and maturity of technology, it is inevitable that artificial intelligence will penetrate into all processes and links of the media industry. 5G, Internet of Things, big data and other technologies It will also open up unlimited space for imagination in the future of the industry. In the future "intelligent media" era, artificial intelligence may not only improve people's efficiency in obtaining information on the Internet, but also help people obtain the information they want in a better and more targeted manner.
The above is the detailed content of Artificial Intelligence in Social Media. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics



This site reported on June 27 that Jianying is a video editing software developed by FaceMeng Technology, a subsidiary of ByteDance. It relies on the Douyin platform and basically produces short video content for users of the platform. It is compatible with iOS, Android, and Windows. , MacOS and other operating systems. Jianying officially announced the upgrade of its membership system and launched a new SVIP, which includes a variety of AI black technologies, such as intelligent translation, intelligent highlighting, intelligent packaging, digital human synthesis, etc. In terms of price, the monthly fee for clipping SVIP is 79 yuan, the annual fee is 599 yuan (note on this site: equivalent to 49.9 yuan per month), the continuous monthly subscription is 59 yuan per month, and the continuous annual subscription is 499 yuan per year (equivalent to 41.6 yuan per month) . In addition, the cut official also stated that in order to improve the user experience, those who have subscribed to the original VIP

Improve developer productivity, efficiency, and accuracy by incorporating retrieval-enhanced generation and semantic memory into AI coding assistants. Translated from EnhancingAICodingAssistantswithContextUsingRAGandSEM-RAG, author JanakiramMSV. While basic AI programming assistants are naturally helpful, they often fail to provide the most relevant and correct code suggestions because they rely on a general understanding of the software language and the most common patterns of writing software. The code generated by these coding assistants is suitable for solving the problems they are responsible for solving, but often does not conform to the coding standards, conventions and styles of the individual teams. This often results in suggestions that need to be modified or refined in order for the code to be accepted into the application

Large Language Models (LLMs) are trained on huge text databases, where they acquire large amounts of real-world knowledge. This knowledge is embedded into their parameters and can then be used when needed. The knowledge of these models is "reified" at the end of training. At the end of pre-training, the model actually stops learning. Align or fine-tune the model to learn how to leverage this knowledge and respond more naturally to user questions. But sometimes model knowledge is not enough, and although the model can access external content through RAG, it is considered beneficial to adapt the model to new domains through fine-tuning. This fine-tuning is performed using input from human annotators or other LLM creations, where the model encounters additional real-world knowledge and integrates it

To learn more about AIGC, please visit: 51CTOAI.x Community https://www.51cto.com/aigc/Translator|Jingyan Reviewer|Chonglou is different from the traditional question bank that can be seen everywhere on the Internet. These questions It requires thinking outside the box. Large Language Models (LLMs) are increasingly important in the fields of data science, generative artificial intelligence (GenAI), and artificial intelligence. These complex algorithms enhance human skills and drive efficiency and innovation in many industries, becoming the key for companies to remain competitive. LLM has a wide range of applications. It can be used in fields such as natural language processing, text generation, speech recognition and recommendation systems. By learning from large amounts of data, LLM is able to generate text

Machine learning is an important branch of artificial intelligence that gives computers the ability to learn from data and improve their capabilities without being explicitly programmed. Machine learning has a wide range of applications in various fields, from image recognition and natural language processing to recommendation systems and fraud detection, and it is changing the way we live. There are many different methods and theories in the field of machine learning, among which the five most influential methods are called the "Five Schools of Machine Learning". The five major schools are the symbolic school, the connectionist school, the evolutionary school, the Bayesian school and the analogy school. 1. Symbolism, also known as symbolism, emphasizes the use of symbols for logical reasoning and expression of knowledge. This school of thought believes that learning is a process of reverse deduction, through existing

Editor |ScienceAI Question Answering (QA) data set plays a vital role in promoting natural language processing (NLP) research. High-quality QA data sets can not only be used to fine-tune models, but also effectively evaluate the capabilities of large language models (LLM), especially the ability to understand and reason about scientific knowledge. Although there are currently many scientific QA data sets covering medicine, chemistry, biology and other fields, these data sets still have some shortcomings. First, the data form is relatively simple, most of which are multiple-choice questions. They are easy to evaluate, but limit the model's answer selection range and cannot fully test the model's ability to answer scientific questions. In contrast, open-ended Q&A

Editor | KX In the field of drug research and development, accurately and effectively predicting the binding affinity of proteins and ligands is crucial for drug screening and optimization. However, current studies do not take into account the important role of molecular surface information in protein-ligand interactions. Based on this, researchers from Xiamen University proposed a novel multi-modal feature extraction (MFE) framework, which for the first time combines information on protein surface, 3D structure and sequence, and uses a cross-attention mechanism to compare different modalities. feature alignment. Experimental results demonstrate that this method achieves state-of-the-art performance in predicting protein-ligand binding affinities. Furthermore, ablation studies demonstrate the effectiveness and necessity of protein surface information and multimodal feature alignment within this framework. Related research begins with "S

According to news from this site on August 1, SK Hynix released a blog post today (August 1), announcing that it will attend the Global Semiconductor Memory Summit FMS2024 to be held in Santa Clara, California, USA from August 6 to 8, showcasing many new technologies. generation product. Introduction to the Future Memory and Storage Summit (FutureMemoryandStorage), formerly the Flash Memory Summit (FlashMemorySummit) mainly for NAND suppliers, in the context of increasing attention to artificial intelligence technology, this year was renamed the Future Memory and Storage Summit (FutureMemoryandStorage) to invite DRAM and storage vendors and many more players. New product SK hynix launched last year
