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Studying audience behaviour in mobile news applications that offer personalised content requires a systematic approach involving data collection, data processing, pattern identification, and actionable insights. As a media researcher, the focus would be on leveraging both Data Science and Big Data techniques to understand how users interact with the app, what content they prefer, and how their engagement changes over time. This process involves integrating structured data (e.g., article categories, timestamps) with unstructured data (e.g., article text, multimedia content) and applying advanced analytical methods to uncover meaningful behavioural patterns. The first step would involve data acquisition. The mobile news app can generate massive amounts of user interaction data, such as the time spent on specific articles, the number of times a user visits the app per day or week, categories of articles read (e.g., politics, sports, entertainment), search queries, video plays, likes, shares, and even scroll depth within articles. This data would be collected in raw form from user logs, clickstream data, and app analytics systems. Since this involves potentially millions of users and billions of data points, Big Data storage technologies such as Hadoop Distributed File System (HDFS) or cloud-based platforms like AWS S3, Azure Data Lake, or Google BigQuery would be essential for storing and retrieving the data efficiently. Next would be data preprocessing and cleaning, which is crucial before any analysis. The raw data often contains noise, inconsistencies, and missing values. For example, session times might be incomplete if a user closes the app abruptly, or article categories might be incorrectly tagged. Data Science techniques such as handling null values, normalising timestamps, ___ ___ _____ _____ ______ _________ ___.
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