When it Rains, it Pours: Modeling Media Storms and the News Ecosystem (2023)

Abstract: Most events in the world receive at most brief coverage by the news media. Occasionally, however, an event will trigger a media storm, with voluminous and widespread coverage lasting for weeks instead of days. In this work, we develop and apply a pairwise article similarity model, allowing us to identify story clusters in corpora covering local and national online news, and thereby create a comprehensive corpus of media storms over a nearly two year period. Using this corpus, we investigate media storms at a new level of granularity, allowing us to validate claims about storm evolution and topical distribution, and provide empirical support for previously hypothesized patterns of influence of storms on media coverage and intermedia agenda setting.

## introduction on march 9, 2021 jury selection began in the trial of minneapolis police officer derek chauvin. over the next month and a half, details of the case such as calls for delay, jury selection, presentation of evidence, and an eventual guilty verdict were covered scrupulously by the media-peaking in coverage 48 days after the story first broke (see figure 1 ). this type of enduring and high volume coverage is a prime example of a media storm. in this paper, we construct a comprehensive corpus of media storms from nearly two years of coverage, which we use to study the nature and dynamics of this particularly influential media event type. citizens are exposed to major news stories such as these on a daily basis, and these news events have been shown to influence public discussion and debate @xcite @xcite . furthermore, decades of scholarship have attempted to characterize this type of coverage under a range of theoretical definitions such as media hypes, political waves, and news waves @xcite march 4, 2021 march 30, 2021 april 26, 2021 elmelund-praestekaer, 2009; @xcite . we build especially on the work of @xcite , who approach this topic by synthesizing previous theoretical definitions under the term media storms, focusing on coverage so prominent that media consumers can't help but know about it. while prior research has theorized about their mechanisms, there has been limited quantitative analysis of the conditions in which storms emerge, and the effects that they have on the news ecosystem. through the creation of a state-of-the-art news article similarity model, we identify story clusters from a corpus of over 4 million news articles. within this larger dataset, we identify 98 media storms over a period of 21 months; while small in number, these storms alone comprise 20,331 articles in our data, and were covered widely in 417 mainstream, conspiratorial, political, and local news outlets. the diversity and magnitude of our media storms provide a unique opportunity to test specific hypothesis from media and communications theory. in brief, this work makes the following contributions: we create a state-of-the-art article embedding model enabling fast and accurate comparison of billions of news article pairs, and use it to create and publicly release a new dataset of story clusters, augmenting a combination of existing news datasets representing almost two years of coverage ( §3). working at a more fine grained level of analysis than was previously possible, we then validate and strengthen previous observations about the temporal dynamics ( §4) and topical composition ( §5) of media storms. finally, we provide evidence about the role of media storms in regard to intermedia agenda setting and gatekeeping patterns amongst news outlets ( §6). given the influence of media storms and other news on democratic debate and public discourse @xcite , our findings also offer a step towards answering the question of which actors get to decide what information becomes impossible to ignore. furthermore, the introduction of our model allows researchers to trace the evolution of single stories and events over time, enabling a number of new downstream analyses in the agenda setting and framing space. our model for predicting article similarity, as well as data and replication code for this paper, are available at https://github.com/blitt2018/mediastorms . ## background defining media storms a number of definitions have been employed to characterize explosive and long-lasting stories that arise in the news ecosystem @xcite . @xcite present the idea of news waves, which are started by a key event and trigger increased coverage of similar events. @xcite builds off of the news wave phenomenon, introducing media hypes. whereas news waves are identified based on an increase in coverage relative to the number of events, media hypes are defined by a self-reinforcing process of news production resulting in an increased number of articles pertaining to a single issue. @xcite 's media storm abstracts the descriptive claims of prior theories while relaxing assumptions about the mechanisms behind them. theoretically, media storms are when the media ecosystem goes into "storm mode", shifting from routine coverage to an intensive focus on a particular event, issue, or topic over a period of time @xcite . mechanisms two primary mechanisms have been identified to explain the development of media storms. first, media storms may be caused by a lowering of the gatekeeping threshold @xcite @xcite . after an event or issue has received extensive coverage at a given outlet, it is seen as more newsworthy by reporters and editors at that same outlet, who will therefore be more likely to write or publish additional coverage of it. this notion is supported by evidence that organizations tend to operate with a centralized, top down structure in the wake of large events, which implies that certain trigger events cause a large spike in follow up coverage and a subsequent media storm @xcite . while the gatekeeping threshold primarily concerns within-outlet reporting practices, the intermedia agenda setting hypothesis describes the emulation of coverage between outlets. when outlets see that a story has risen to prominence in other outlets, they may be incentivized to shift their own coverage @xcite @xcite @xcite . this incentivization may be especially true for outlets which feel that their principal reporting domains are being encroached upon @xcite . ## creating a media storms corpus to investigate media storms at scale, we augment a set of three large and diverse datasets by creating a model to group news articles into story clusters. ## characterizing media storms our definition and selection process focuses on media outlets reporting in storm mode over a substantial period of time. we now characterize which feature min max mean median number of articles 51 1378 207.5 156 duration (days) 7 54 15.0 11 number of outlets 5 200 76.1 74 % national 0 100 59.1 62 ## topical distribution of storm coverage examining media storms at the level of issues, @xcite hypothesized and found that media storms tend to be more skewed towards certain issues than overall media coverage, with the majority of storms being related to government operations, defense, or international affairs (which were also the most common issues in overall coverage, but to a much lesser degree). 7 this observation also aligns with literature positing that stories with certain news values are more appealing to the media and therefore more likely to trigger storm-development mechanisms @xcite . to test this theory at the level of media storms tied to specific events, we ran latent dirichlet analysis @xcite on our entire corpus of news articles using mallet @xcite . we set the number of topics to be 30, infer a distribution over topics for each article, and associate each article with its highest-scoring topic. finally, we aggregate these topics across storm and non-storm articles. as shown in figure 5 and supplemental figures 11 and 12 in appendix c, we confirm the previous finding that media storms have a topic distribution which is far more skewed than the topic distribution for non-storm articles. in addition, we find that certain topics make up a much larger fraction of coverage for storms than non-storms. in particular, topics related to politics, natural disasters, and policing are overrepresented in storms, whereas topics related to sports, entertainment, and daily life are underrepresented. in contrast to @xcite , however, we find that topics related to defense and international affairs are less common among storms compared to non-storms, as well as being less common overall, compared to other topics (see figure 12 in appendix c). this may relate in part to our use of a much wider set of media outlets, including many local news sources. 5 0 5 10 storm topic % -non-storm topic % court,case,law bill,state,house election,state,voters trump,president,house water,climate,fire police,officers,man trump,biden,president vaccine,covid,health media,news,facebook covid,coronavirus,cases california,san,border military,air,war game,season,games school,students,schools people,health,work a.m,family,p.m store,amazon,home it s,game,season russia,russian,military china,world,chinese team,points,game people,world,black city,county,council p.m,event,year show,film,music england,league,london people,time,family million,year,money regarding the topic skew of media storms, we hypothesize that this distinction may be a result of certain types of events lending themselves to ongoing and follow-up coverage. a topic like entertainment may contain more disparate one-off events, whereas legislation and disasters reflect events whose development and consequences can last for longer periods of time. ## mechanisms of storm development having provided evidence for media storms as a distinct temporal and topical phenomenon, we turn toward testing two mechanisms associated with storm development. first, we test whether storms are associated with a lowering of the gatekeeping threshold-an increased recognition of the newsworthiness of a particular topic. second, we test whether media storms are associated with intermedia agenda-setting behavior, a phenomenon wherein outlets mimic one another to keep their coverage relevant. ## discussion when considered in the context of prior work, our results have implications for the media's effect on public and congressional agendas. in their original paper, @xcite find that google queries corresponding to a given event spike more during media storms than during coverage of similar non-storm stories. furthermore @xcite find that news stories exert a stronger influence over the congressional agenda when these stories are part of a media storm. by characterizing the temporal dynamics, topical skew, and lead-lag relationships associated with media storms at the level of events, we give context for what types of stories have a larger agenda setting effect and where these stories originate from. although our analysis of media storms revealed a somewhat different distribution of topics than the hand-coded analysis of news headlines in @xcite , overall our characterization of storms broadly matches theirs. some events, such as accusations of sexual assault against joe biden, trigger media storms involving hundreds of news articles across dozens of outlets. others, such as airlines cancelling flights due to staffing shortages, have a much smaller footprint, but still persist in the news cycle for more than a week. while some storms, (such as the trial of derek chauvin), peak weeks after they begin, the typical storm follows a pattern of explosive coverage (peaking within the first few days), followed by a gradual decline in attention. moreover, this initial increase in coverage is accompanied by, and sometimes even anticipated by, an increase in attention to the broader issue area that the storm is part of (e.g., policing, natural disasters, etc.). all of these findings point to the operation of underlying mechanisms governing news production, with limited resources available within each news outlet. beyond the results in this paper, the methodology, model, and dataset we contribute open the door for future research on media storms. one important direction is the causal investigation of media influence. for example, identification of matched pairs of media storms with comparable non-storms would allow for more direct testing of the key conditions which cause stories to gain traction. such a study is difficult because many media storms may have no comparable counterfactual non-storm, and the relevant features needed to match pairs may be unobserved. unanticipated real world events (e.g. natural disasters) may also provide natural experiments to study the media conditions in which storm are most likely to develop, and could be compared to storms based around events which can be anticipated. alternatively, follow up studies could investigate how interactions between traditional media, social media, and political elites influence the trajectory of media storms. in addition to the estimation of larger and more diverse diffusion networks, future work could also connect these results with the literature on framing and agenda setting. our analysis finds that unreliable media frequently lags behind other media sources; however, it is unclear why this is the case. past work has hypothesized that alternative media frequently frames itself in opposition to the mainstream, revealing the "truth" that the mainstream is "unwilling" to say @xcite . if the framing of outlets' coverage can be connected to patterns of diffusion, this would result in a far more complete picture of why media outlets report what they report when they do. finally, future work may address the limitations of our news similarity model and article matching pipeline. in terms of efficiency, a faster article matching approach would allow for analysis of the much larger, global media ecosystem. in terms of accuracy, our model's performance is typically lower on articles generated from templates such as covid-19 trackers. a more sophisticated iteration would reliably distinguish between articles about different events in spite of their high textual similarity. ## conclusion news organizations shape the public perception of what issues are most salient and media storms play an important role in saturating coverage on a single story. here, we introduce a new computational approach to quantifying storms and their influence across nearly two years and 4 million news articles, using a new state-of-the-art model to identify news articles on the same event. using the 98 storms in our data, we show how they migrate through the media ecosystem via structured lead-lag relationships between outlets and how their development is associated with increased interest in the broader topic or issue that the storm is about. taken together, our results distinguish media storms as a phenomenon with distinct temporal and topical characteristics. furthermore, they indicate that some news outlets and categories of news are seemingly more influential than others. these findings give insight into how large news stories form and spread, providing a stepping stone for further computational investigation.