Jonathan J. H. Zhu

We report a systematic review of longitudinal studies published in SSCI-Communication journals over the 50 years, focusing on how time is conceived, measured, and analyzed as a scientific concept. To facilitate the review, we devised two novel classification schemes to distinguish between longitudinal and non-longitudinal studies and to measure how time is treated. We manually coded a random sample of 1,080 research articles, supplemented with computerized keyword scanning of the study population. The results show that longitudinal studies have steadily accounted for about 9% of the publications. Panel data studies outnumber time-series studies in a ratio of 3:2. Three-fourths of the longitudinal studies treat time as a timing index and aim to test causal effects, whereas a quarter treat time as a parameter to capture temporal dynamics. Longitudinal studies make more conceptual contributions and receive more citations than non-longitudinal studies. We offer practical recommendations for longitudinal communication research.

Ecology is a scientific discipline focusing on interactions among living organisms and their environment. We advocate for applying ecological theory to communication research, using three notable debates for illustration: issue competition, media convergence, and AI-generated content (AIGC). For the first debate, we review how we developed and tested an ecology-informed framework to determine the competitive nature of the public issue agenda. For the second debate, we demonstrate parallel trends between media convergence and divergence through case studies of the Apple ecosystem and the Time-Warner-AOL merger. For the third debate, we formulate hypotheses about AIGC's ecosystem, creativity, emotional intelligence, and the replacement of human-generated content, drawing on mediamorphosis and other ecological principles. While complementing the existing theories and methods in communication research, the ecological approach sheds unique insights into long-standing puzzles and emerging unknowns.

Computational communication research (CCR) has emerged as an early and active subfield within computational social science, offering novel methodological approaches to address classical and emerging questions in communication research. This paper reviews the evolution and methodological developments in CCR in both the international research community and Chinese mainland, emphasizing the role of computational methods such as user analytics, content mining, and computational experiments. We map the alignment between traditional quantitative methods and computational ones, demonstrating how CCR methods have complemented and enhanced existing research paradigms. Through empirical applications, we illustrate the utility of CCR in understanding user behavior, content dynamics, and communication effects. We conclude with a discussion on the challenges of using computational methods in the Chinese context and the future directions for CCR.

This research holds significance for advancing financial forecasting methodologies by shifting the focus from traditional sentiment analysis of individual tweets to exploring intricate semantic relationships within news tweets from top-followed news channels on Twitter. Addressing a notable research gap in financial forecasting, often dominated by sentiment analysis, our study endeavors to fill the void left by the underexplored intricate relationships within news entities and their dynamic semantic evolution. Motivated by the inherent challenges in predicting the random walk behavior of stock prices, we contend that incorporating longitudinal data derived from the semantic relationships between news entities can enhance the accuracy of stock market forecasts. The study pioneers a twelve-year exploration, encompassing data from 55 leading news channels on Twitter, boasting a collective following of 714 million users. The approach employs natural language processing (NLP) to extract two million unique entities, whose semantics are analyzed through complex network analysis, laying the foundation for the forecasting model. Finally, this research introduces a model linked to the dynamic semantic structure of news flow. The predictive model considers the impact of exogenous variables influenced by the evolving relationships among news entities. The results offer a proof of concept, highlighting the potential of utilizing dynamic semantic relationships among news entities for financial prediction. On average, the model demonstrates an improvement in accuracy of 40.3% across ten different stock price predictions. These findings are expounded through relevant theories, offering a theoretical foundation for observed patterns and indicating a promising direction for future research in this domain.

This research holds significance for the fields of social media and communication studies through its comprehensive evaluation of Twitter’s quoting encouragement policy enacted during the 2020 U.S. presidential election. In addressing a notable gap in the literature, this study introduces a framework that assesses both the quantitative and qualitative effects of specific platform-wide policy interventions, an aspect lacking in existing research. Employing a big data approach, the analysis includes 304 million tweets from a randomly sampled cohort of 86,334 users, using a systematic framework to examine pre-, within-, and post-intervals aligned with the policy timeline. Methodologically, SARIMAX models and linear regression are applied to the time series data on tweet types within each interval, offering an examination of temporal trends. Additionally, the study characterizes short-term and long-term adopters of the policy using text and sentiment analyses on quote tweets. Results show a significant retweeting decrease and modest quoting increase during the policy, followed by a swift retweeting resurgence and quoting decline post-policy. Users with fewer connections or higher activity levels adopt quoting more. Emerging quoters prefer shorter, positive quote texts. These findings hold implications for social media policymaking, providing evidence for refining existing policies and shaping effective interventions.

Communication research has been one of the fastest-growing disciplines across the social sciences over the last two decades in terms of the numbers of Social Science Citation Indexed journals and articles. However, whether Communication is an independent discipline remains debated. Of various criticisms, one extreme considers Communication too dependent on other disciplines, whereas the other regards Communication as too inward-looking. In the current study, we measure and analyze citations of articles not only among communication scholars but also between communication scholars and their counterparts from other disciplines to evaluate the performance of communication research. Our findings suggest that communication research has maintained balanced citation patterns, with a 20% self-citation rate, a 1:1 ratio between incoming and outgoing citations, and a high diversity of in- and out-citations across social science disciplines. The results may serve as useful food for thought for future evaluation of communication discipline.