A New Era of Seeking Knowledge for #Lymphedema on Social Media: A Detailed Instagram Hashtag Analysis

Tuğral, Alper; Eliiyi, Uğur; Özdemir, Kadirhan; Ergin, Gülbin; Bakar, Yeşim

doi:10.2458/lymph.4728

Cited by 5 publications

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References 4 publications

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“…By leveraging data analysis, we aim to demystify the process of improving visibility and engagement on Instagram, ultimately helping content creators achieve greater success on the platform. [1] In the study "A New Era Of Seeking Knowledge For Lymphedema On Social Media: A Detailed Instagram Hashtag Analysis", data collection from Instagram was meticulously conducted using an automated scripting tool for web crawling and data scraping. Nine predefined hashtags related to lymphedema were employed to filter posts, and subsequent analysis focused on assessing content quality and interaction statistics.…”

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confidence: 99%

Boosting Instagram Visibility with Machine Learning

2024

IJSREM

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Boosting Instagram Visibility project aims to enhance the visibility of Instagram posts by leveraging machine learning techniques. We follow a systematic process to analyze data gathered from Instagram posts, including details like post content, engagement metrics, and usage of hashtags. Firstly, we collect relevant data from Instagram through various means, ensuring we capture essential information such as post text, engagement metrics, and user demographics. This serves as the foundation for our analysis. Next, we preprocess the collected data to ensure its quality and consistency. We clean the data, handle missing values, and preprocess text to make it suitable for analysis. We then select or extract meaningful features from the data that are crucial for predicting post visibility. These features could include post content, engagement metrics, timing, and demographic information. With the features identified, we choose appropriate machine learning algorithms to analyze the data. These algorithms are selected based on the nature of the prediction task and the characteristics of the dataset. We train the selected machine learning model using the preprocessed data, splitting it into training and validation sets to evaluate the model's performance. After training, we evaluate the model's performance using metrics such as mean absolute error (MAE) or accuracy. This helps us understand how well the model predicts post reach based on the provided features. Using the trained model, we make predictions on new or unseen data, providing insights into the potential visibility of Instagram posts. Through interpretation and analysis of the model predictions, we gain insights into the factors that contribute most to post visibility, helping users optimize their Instagram strategies. Finally, we iterate on the model and process based on feedback and insights gained, continuously refining our approach to improve prediction accuracy. Key Words: Engagement metrics, Hashtags, Mean absolute error, Post visibility, Optimization strategies

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confidence: 99%