Neuro-AI: Enhancing Digital Growth Metrics through Social Media Automation

doi:10.21203/rs.3.rs-3323718/v1

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Neuro-AI: Enhancing Digital Growth Metrics through Social Media Automation

https://doi.org/10.21203/rs.3.rs-3323718/v1

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The introduction of social media platforms transformed the way individuals and businesses engage, communicate, and advertise their goods and services. Understanding and anticipating the growth metrics of online content is critical for keeping a competitive edge in this continuously developing digital market. Using Artificial Intelligence (AI) based on neurological patterns, this article proposes a novel technique to predicting digital growth indicators such as post engagement, reach, and user interactions. The discussed research topics are implemented using a provided approach, and the findings are discussed, including roughly 91% correctly predicted hashtags, 0.7 R² scores, and an acceptance rate of approximately 78% by various influencers for the created post content. For these results, I collaborated with ten influencers ranging in size from small to medium who were willing to work on this method in private because data cannot be disclosed publicly.

Neurological Prediction

Digital Growth Metrics

Artificial Intelligence

Social Media Automation

Social Media Platforms

User Engagement

The creation of content and use of social media for commercial purposes is a vast and mature industry. As depicted in Fig. 1, the importance of social media is increasing as most branding efforts and marketing budgets are allocated to the use of digital social platforms to communicate with the target audience. In today's fast-paced digital era, the significance of data-driven decision making is becoming increasingly apparent across all industries. Businesses and organizations are always attempting to forecast and optimize their digital growth metrics, such as website traffic, user engagement, conversion rates, and revenue generation. Simultaneously, advancements in artificial intelligence (AI) and neuroscience have created new avenues for comprehending the human brain and its intricate relationship with technology. Neurological prediction of digital growth metrics utilizing artificial intelligence is a cutting-edge discipline that combines neuroscience, data analytics, and AI algorithms to predict and improve various aspects of digital growth. Researchers and practitioners are exploring ways to obtain valuable insights into human behavior, preferences, and responses in the digital environment by leveraging the power of AI and analyzing neural signals. Historically, digital growth metric forecasts have been based on historical data, statistical models, and market trends. While these approaches have produced valuable insights, they frequently lack a thorough comprehension of the cognitive and emotive factors that influence user behavior. Neurological prediction, on the other hand, seeks to bridge this gap by directly examining the neurological processes underlying digital interactions. Researchers can measure and analyze brain activity in response to digital stimuli by employing cutting-edge technologies like electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and machine learning algorithms. These neural signals can provide important information regarding user engagement, attention levels, emotional responses, cognitive burden, and decision-making processes. Integrating these neural insights with AI models enables companies to make more accurate predictions and optimize their digital expansion strategies.

Neurological prediction of digital growth metrics has extensive application potential. It can assist businesses in optimizing website and app design, personalizing user experiences, targeting marketing campaigns, increasing customer satisfaction, and boosting conversion rates. By understanding how users' brains react to various digital elements, businesses can better tailor their products and services to satisfy the needs and preferences of customers. However, this emerging discipline also presents ethical challenges. Concerns about privacy, consent, and the responsible use of personal information are raised by the collection and analysis of neural data. To safeguard the rights of users and preserve the reliability of neuro-prediction techniques, it is necessary to implement ethical guidelines and regulations.

For businesses seeking to optimize their digital strategies, the neural prediction of digital growth metrics aided by artificial intelligence holds great promise. By combining neuroscience and AI, businesses can obtain a deeper understanding of user behavior, enhance their decision-making processes, and improve their digital experiences overall. However, it is essential to approach this discipline with an ethical mindset and to ensure the responsible utilization of neurological data. With ongoing research and technological advancements, the digital future of neurological prediction appears extremely promising.

This research provides digital support for influencers and content creators by analyzing trending hashtags with artificial intelligence and providing influencers with crucial insights. This investigation is being conducted because I want to be active on social media and require a higher profile rank. This research also examines various previous works in the form of a literature review, provides a methodology for the system's development, and presents the system's implemented results.

Smith et al. (2018) used EEG data gathered during interactions with an online platform to examine the neurological correlates of user engagement. The authors discover brain indicators linked to high levels of engagement, providing insights for forecasting and optimizing digital growth measures.

Garcia et al. (2019) studied the use of fMRI-derived brain data to predict conversion rates in online advertising. They create a machine learning model that uses brain signals to precisely predict user behavior, resulting in better digital growth metrics.

Lee(2020) et al. used fMRI data to better understand emotional responses to digital information. The study shows how brain signals can be used to predict emotional involvement and improve digital growth measures.

Wang et al. (2017) investigated EEG data to find neurological predictors of website usability. They create a prediction model based on brain signals, which provides useful insights for improving user experience and digital growth measures.

Liu(2021) et al. investigated the use of EEG-captured neural signals to personalize recommendations in digital platforms. The authors suggest a neural-based recommendation system that increases user satisfaction while also improving digital growth indicators.

Davis et al. (2019) studied the use of EEG in virtual reality environments to quantify attention and cognitive effort. The findings illustrate the potential of utilizing neural indicators in immersive experiences to optimize user engagement and digital growth metrics.

Chen et al. (2020) examined fMRI data to better understand brain reactions to persuasive messages in digital advertising. The research sheds light on the neurological mechanisms that underpin user engagement and has implications for enhancing digital growth measures.

Zheng et al. (2018) studied the use of EEG-collected brain data to predict user happiness in mobile applications. The authors create a neural signal-based predictive model that allows for the optimization of digital growth measures in mobile situations.

Kim S. et al. (2019) used EEG data to find neural determinants of user desertion in e-commerce websites. The study provides useful information for lowering abandonment rates and increasing digital growth metrics in online retail.

Martinez et al. (2022) studied the use of EEG-derived brain signals to predict user participation in social media platforms. The authors create a neural data-based predictive model that provides insights for optimizing digital growth metrics in social media situations.

Johnson et al. (2021) used fMRI data to investigate the neural indicators of trust and trustworthiness in online platforms. The findings help to understand user behavior and have implications for improving digital growth metrics in trust-sensitive scenarios.

The study will use a mixed-method research methodology that will integrate quantitative and qualitative methodologies. This will allow for a thorough examination of neurological data and user engagement metrics, as well as important insights into user behavior and preferences via surveys and interviews.

3.1 Data Gathering

Neurological Data: Participants' neurological data will be collected using non-invasive wearable equipment such as EEG (Electroencephalogram) headsets and biometric sensors. While consumers interact with diverse social media content, neurological signals will be captured.
Engagement Metrics: Through APIs or web scraping techniques, digital growth metrics such as post engagement, reach, user interactions, and other relevant analytics will be acquired from social media platforms.
Demographic Information: Surveys or user profiles will be used to acquire additional data on participants' demographics, social media activity patterns, and content choices.

Selection of Participants: A varied sample of social media users will be chosen to ensure representation across demographics, age groupings, and content interests. Prior to data collection, participants' consent will be acquired, and their privacy will be respected during the project.

3.2 Analyzing Neurological Data

Preprocessing: Artifacts, noise, and outliers will be removed from the obtained neurological data during preprocessing.
Feature Extraction: Relevant features from the neurological data, such as brainwave patterns and physiological responses, will be retrieved to represent the cognitive states of the individuals.
Machine Learning: To create a predictive model, advanced machine learning methods, such as deep learning models, will be used. The neural data will be used to train the model, which will look for patterns between cognitive states and subsequent user involvement.

3.3 System for Social Media Automation

Development: Based on the predictive AI model, an automation system will be devised and developed. Based on projected neurological responses, the technology will dynamically customize content delivery on social media sites to improve user engagement.
Integration: To enable real-time content personalization and distribution, the AI model will be incorporated into the automation system.

3.4 Validation and evaluation

Cross-Validation: To assure accuracy and generalization capabilities, the predictive AI model's performance will be evaluated using cross-validation techniques.
A/B Testing: The effectiveness of the social media automation system will be assessed using A/B testing, which will compare engagement metrics with and without AI-driven content personalization.

Ethical Considerations: Throughout the research process, the study will follow ethical criteria to ensure participant anonymity, data privacy, and consent. Relevant ethical committees will provide the necessary authorization. Data Analysis and Interpretation: The engagement metrics, neurological data, and survey responses obtained will be evaluated to derive conclusions about the relationship between neurological patterns and digital growth metrics. To get insights into user preferences and experiences, qualitative data will be thematically evaluated.

The VAE-GAN (Variational Autoencoder-Generative Adversarial Network) algorithm, which takes in data about numerous artists, their social media metrics, behavior, and performance, drives the research. It then maps that data with the various required labels in order to estimate output in the form of projected metric figures particularly for followers of the specified account (see Fig. 2).

Data which once collected by way of generating traffic on the web portal for interacting with social media gets processed, cleaned, transformed, and staged to make the corpus of data. This same data also gets passed through Variational Autoencoders to synthesize new similar data records to increase the volume. The final corpus gets passed through the Generative Adversarial Network to predict the final (approved by GAN) output as showed in Fig. 3.

Post completion of the final training of the model followed by validation and performance check the model gets serialized and passed to Python so that it can be deployed in a Django-based application for usage on the client side. The proposed flow of methodology is mentioned in Fig. 4

3.4 Experimental Testing and Setup

a) Experiment with Sentiment Analysis:

List of social media posts(comments, likes and shares) is compiled after that Sentiment analysis on the collected data, use Python libraries such as NLTK or transformers is done. Sentiment scores is calculated (positive, negative, and neutral) and sentiment distribution is investigated. Neuro-AI for sentiment analysis aids in improvement of understanding of user reactions and emotions.

b) Test for Personalized Content Recommendations:

Creation of a Python-based content recommendation system is done that leverages AI-based algorithms to personalize content suggestions for users based on their previous interactions.

Audience is divided into experimental groups, and give each group a different set of personalized content recommendations Engagement metrics is targeted in this process.

c) Use of Python automaton tools

Using Python automation tools like Tweepy or social media APIs, the frequency and time of your social network updates is changed. Grouping of audience is done into numerous groups, including one with no social media automation and others with Neuro-AI-powered automation tactics. Implementation of Python scripts is done for each group to automate posting and content curation.

Over a given time period, track and compare growth indicators such as follower count, reach, and engagement.

d) Statistical Tests

Use statistical tests to see if the Neuro-AI automation has a substantial impact on growth indicators when compared to the control group.

e) Analysis of User Behavior:

Python can be used to track user interactions and behaviors on social media platforms, such as clicks, time spent on postings, and website navigation.

f) Longitudinal Research:

A long-term study is conducted for spanning several weeks or months to examine the long-term impact of Neuro-AI-powered social media automation on digital growth KPIs.

Finally, our proposed model was rigorously tested and evaluated by ten different influencers from various industries and content categories. The entire task was separated into divisions, and the results of this investigation were thoroughly detailed in this section. The trending hashtag categorization service performed admirably, with an amazing aggregate accuracy of 91%. This means that 91 of the 100 trending hashtags were discovered to be trending on social media platforms. Such a high accuracy rate is a remarkable accomplishment and acts as a critical step in carrying out additional execution and analysis as per Fig. 5. Throughout the study process, we ran many experiments by adjusting various parameters in order to improve the accuracy of the trending hashtag categorization service. Our unwavering efforts seek to enhance efficiency and outperform existing approaches. We were able to get higher precision by fine-tuning the settings, demonstrating the efficiency of our technique. This study's findings show promise for the fields of social media analytics and influencer marketing. The accurate identification of trending hashtags can greatly assist content creators, influencers, and businesses by allowing them to adjust their content to current trends and reach a larger audience. While the findings of this study are positive, we recognize that additional research and adjustments can be made to improve the application's performance even further. To keep up with new trends and user behaviors, social media dynamics need regular updates and enhancements

- For future follower prediction, the R² score obtained is 0.72, and the projected values are generally within 13% of the actual value. As seen in Fig. 6, the lines in orange indicate the real data, while the lines in blue represent the deviated data, which is a 13% deviation from the value.

- Influencers accepted 78% of the posts provided to them on average. Each influencer received 20 posts created by the algorithm, which they chose between 12 and 18, with an average of 15.6 posts (i.e. 78%). The green zone in Fig. 7 indicates accepted postings, whereas the red region represents rejected postings.

Six out of ten influencers thought this was a good tool for their purposes and agreed to utilize it in their job.

In conclusion, our research has advanced the accuracy and efficiency of trending hashtag categorization significantly. We believe that our findings have significant implications for social media marketing strategies and content creation, providing actionable insights to help influencers and businesses achieve increased online visibility and engagement. As we continue to improve and expand on these findings, we hope to contribute to the ever-changing environment of social media analytics and Artificial Intelligence applications. The study concentrated on several neurological factors related with the aid of influencers in predicting social media metric growth (particularly, follower count, likes, comments, and share counts, etc...). The creative business is expanding at such a rapid pace that it is hard to estimate it manually. This study concludes that using automated metric assistants is feasible and acceptable to the various influencers listed in the results. This research, however, can be expanded in the direction of various tangents such as determining the best time to post a given piece of content, providing various information resources from similar content creators and converting that information into insights, analyzing and estimating the right target audience for a given advertisement campaign launched by content creators, and so on.

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No competing interests reported.

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Neuro-AI: Enhancing Digital Growth Metrics through Social Media Automation

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Abstract

Figures

1. Introduction

2. Literature Review

3. Research Methodology

3.1 Data Gathering

3.2 Analyzing Neurological Data

3.3 System for Social Media Automation

3.4 Validation and evaluation

3.4 Experimental Testing and Setup

4. Results and Discussion

5. Conclusion and Future scope

References

Additional Declarations

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