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Key research themes
1. What are the primary challenges in preparing and collecting social media data for effective social media analysis?
This research theme addresses the crucial preliminary stages of social media analytics, focusing on data discovery, collection, preparation, and cleansing. It matters because the quality and relevance of social media data directly impact the validity and reliability of subsequent analytical insights. The challenges include managing data volume, filtering irrelevant content, handling unstructured multimedia data, and ensuring contextual relevance, which are fundamental bottlenecks before any meaningful analysis can be conducted.
Key finding: Identifies volume of data as the most cited challenge in social media analytics, emphasizing that while many studies address data analysis methods, there is insufficient research on early stages of dataset creation such as... Read more
Key finding: Proposes a human-in-the-loop, systematic pipeline design approach to improve social media data preparation by iteratively refining data cleaning and filtering components. The approach evaluates data quality and relevance at... Read more
Key finding: Demonstrates that handling the heterogeneity of social media data, including variances in platform data structures and unstructured content, requires tailored tools and frameworks. Emphasizes challenges posed by different... Read more
2. How can social network analysis (SNA) deepen understanding of social interactions and influence dynamics on social media platforms?
Research under this theme investigates the structural characteristics of social media user interactions by applying social network analysis techniques to capture relational dynamics, influence patterns, and discourse formations. It targets the dynamic, transient social contexts embedded in digital communication and aims to elucidate how micro-level interactions aggregate into macro-level network structures, which are vital for understanding information diffusion, influence, and community building in online environments.
Key finding: Argues that traditional generalized variables inadequately capture the context-dependent nature of social media behavior; proposes SNA as a method to model dynamic and transient social contexts by converting user interactions... Read more
Key finding: Analyses dialogues and interaction patterns on Twitter from over 640,000 tweets and reveals that about 50% of users actively engage in chats, which are distinctively shorter than other tweet types. Finds that dialogues... Read more
Key finding: Investigates cross-platform influential behaviors, noting that individuals may exhibit consistent or varying influence patterns depending on social media site functionality. The study classifies social media types and... Read more
3. How does social media analytics enable understanding and strategic application of social media data in marketing and organizational contexts?
This theme encompasses applied research targeting integration of social media data into business intelligence and marketing analytics frameworks for value creation. It explores the extraction of consumer insights from large volumes of user-generated content, the impacts of digital communication on organizational strategies, and the ways organizations use social media content to engage stakeholders, understand market dynamics, and enhance competitive advantage.
Key finding: Highlights organizational challenges in leveraging social media data for marketing intelligence, including data integration and culture-related barriers. Presents case studies demonstrating how BI&A tools, when applied... Read more
Key finding: Demonstrates that text mining social media platforms, notably Twitter and Facebook, can yield insights into consumer sentiment, passion, and reach that exhibit statistical associations with firm financial performance. The... Read more
Key finding: Develops an editorial framework to classify and analyze social media content and audience engagement within higher education institutions, revealing strategic content allocation and classification patterns. Shows how such... Read more
Key finding: Provides an empirical comparison of social media usage patterns between Indian Generation Y and Z cohorts, elucidating differential engagement in socialization, information seeking, education, entertainment, and shopping... Read more
Key finding: Uses topic modeling on Twitter data of Portuguese IT startups across funding and product maturity stages to identify thematic content evolution correlating with business life cycle phases. Discovers five key content topics... Read more