Coordinator
Prof. YANG, Tian
Assistant Professor,
School of Journalism and Communication,
The Chinese University of Hong Kong
Prof. LIANG, Hai
Associate Professor,
School of Journalism and Communication,
The Chinese University of Hong Kong
The panel features three distinguished computational social scientists from Stanford University, Northwestern University, and City University of Hong Kong. This panel will discuss the challenges and innovations in digital methods within the post-API era, where obtaining digital data has become increasingly complex due to heightened restrictions on APIs, scraping methods, and anti-AI measures.
Our esteemed speakers will explore how these limitations impact the sustainability of digital research, offering unique perspectives on navigating data acquisition in an age where digital barriers are prevalent. They will discuss cutting-edge approaches to overcoming these obstacles and highlight the importance of sustainable data practices in ensuring robust and ethical research. We will explore the intersection of data, technology, and sustainability, and gain insights from leading scholars who are at the forefront of this evolving field.
Speakers
Prof. CONTRACTOR, Noshir
Jane S. & William J. White Professor of Behavioral Sciences,
McCormick School of Engineering & Applied Science, the School of Communication and the Kellogg School of Management,
Director of the Science of Networks in Communities (SONIC) Research Group,
Northwestern University
Generative AI in Communication Research: Challenges, Opportunities, and Innovations
Abstract: Generative AI has the potential to revolutionize all stages of the communication research process. In the initial phases, AI can assist with comprehensive literature reviews by rapidly analyzing vast amounts of scholarly work and identifying key themes and gaps. It can aid in developing precise research questions and hypotheses by leveraging its understanding of existing research paradigms. AI can facilitate online sampling techniques for data collection and even serve as a surrogate for human respondents in specific scenarios, allowing for more extensive and diverse datasets. In data analysis, AI excels at visual analytics, providing researchers with sophisticated tools to explore complex relationships within numeric, network, and textual data. AI can offer unique perspectives in interpreting results as a virtual collaborator, potentially uncovering insights that human researchers might overlook. AI can be a preliminary reviewer for conference or journal submissions, providing constructive feedback on structure, clarity, and adherence to publication standards. Furthermore, AI can create interactive tools allowing readers to explore research findings dynamically, generate podcasts summarizing key points, and utilize platforms like Google’s NotebookLM to highlight and contextualize crucial takeaways. Integrating AI into research dissemination enhances engagement and comprehension, bridging the gap between complex academic work and diverse audiences. While ethical considerations must be carefully navigated, integrating Generative AI into communication research workflows promises to enhance efficiency, broaden analytical capabilities, and potentially lead to novel discoveries in the field. Importantly, researchers must be transparent in their acknowledgments about how Generative AI was leveraged during the research process. As a discipline, communication research needs to develop comprehensive guidelines for the ethical use of Generative AI in conference and journal submissions to ensure integrity and fairness in scholarly work.
Prof. PAN, Jennifer
Sir Robert Ho Tung Professor of Chinese Studies,
Professor of Communication,
Senior Fellow at the Freeman Spogli Institute for International Studies,
Professor, by courtesy, of Political Science and of Sociology,
Stanford University
Studying Digital Media through Industry Collaboration: The US 2020 Facebook and Instagram Election Study
Abstract: In early 2020, Facebook/Meta partnered with external researchers to investigate how Facebook and Instagram influence key political attitudes and behaviors in the context of the U.S. 2020 elections. This collaboration aimed to address two major challenges hindering research in social media and politics. First, rising public concerns and legal requirements around data privacy have led social media companies to limit data access for external researchers. Second, conducting rigorous scientific studies on social media’s impact is particularly challenging when done retrospectively. This talk will outline the collaboration’s structure, present key findings, and discuss some of the unexpected challenges that emerged.
Prof. ZHU, Jian Hua Jonathan
Chair Professor,
Department of Media and Communication and Department of Data Science,
City University of Hong Kong
Embedding Existing Data as a New Source of Data for Communication Research
Abstract: When speaking of new data for communication research, we usually think of three routes: identifying new sources, developing new methods of data collection, or integrating old and new data. Inspired by the latest large language models (LLMs), there could be a fourth route: transforming conventional tabulated data into an embedding structure. Embeddings enable the representation of high-dimensional data in a lower-dimensional space while preserving inherent relationships, enhancing interpretability, and insight extraction. The approach also facilitates the integration of heterogeneous data from diverse sources. Engineering, business, and a few fields of social sciences have already adopted the approach, providing applicable examples.