When it Rains, it Pours: Modeling Media Storms and the News Ecosystem
Authors: Benjamin Roger Litterer
David Jurgens
Dallas Card
Venue: EMNLP 2023 Findings (2023)
Keywords: political communication
news media
political science
TL;DR: In this work, we employ a news article similarity model to investigate the development and composition of large media events.

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.

When it Rains, it Pours: Modeling Media Storms and the News Ecosystem
Benjamin Litterer
David Jurgens
Dallas Card
School of Information
School of Information and
School of Information
University of Michigan bl blitt@umich.ed edu
Computer Science & Engineering
University of Michigan dalc@umich eddu
University of Michigan jurgensêumi ch. edu
Abstract
250
Number of Articles
200
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 last- ing for weeks instead of days. In this work, 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 cor- pus of media storms over nearly two year period. Using this corpus, we investigate me- dia storms at a new level of granularity, allow- ing us validate claims about storm evolution and topical distribution, and provide empirical support for previously hypothesized patterns of influence of storms media coverage and intermedia agenda setting.
150
100
0.
March 2021
March 30, 2021
April 26, 2021
Figure The evolution of the media storm surrounding Derek Chauvin's trial, shown as the number of articles published about this story per day. Coverage peaked on April 20th, the day a guilty verdict was announced.
Elmelund-Presteeke 2009; Wolfsfeld and Sheafer, 2006; Kepplinger and Habermeier, 1995). We build especially on the work of Boydstun et al. (2014), who approach this topic by synthesizing previous theoretical definitions under the term media storms, focusing on coverage so prominent that media con- sumers can't help but know about it. While prior re- search has theorized about their mechanisms, there has been limited quantitative analysis of the condi- tions in which storms emerge, and the effects that they have on the news ecosystem.
1 Introduction
On March 9, 2021 jury selection began in the trial of Minneapolis police officer Derek Chauvin. Over the next month and half, details of the case such as calls for delay, jury selection, presentation of ev- idence, 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 media storm. In this pa- per, construct 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.
Through the creation of state-of-the-art news article similarity model, we identify story clus- ters from corpus of over 4 million news articles. Within this larger dataset, storms over period of 21 months; while small in number, these storms alone comprise 20,331 articles in our data, and were covered widely 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 Communica- tions theory.
identify 98 media
Citizens are exposed to major news stories such as these on daily basis, and these news events have been shown to influence public discussion and debate (Walgrave et al., 2017; McCombs and Shaw, 1972; King et al., 2017: Langer and Gruber, 2021). Furthermore, decades of scholarship have attempted to characterize this type of coverage un- der a range of theoretical definitions such as media hypes, political waves, and news waves (Wien and
In brief, this work makes the following contri- butions: We create state-of-the-aat article embed- ding model enabling fast and accurate comparison of billions of news article pairs, and use it to create

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and publicly release new dataset of story clus- ters, augmenting combination of existing news datasets representing almost two years of cover- age ($3). Working at a more fine grained level of analysis than was previously possible, we then val- idate and strengthen previous observations about the temporal dynamics ($4) and topical compo- sition ($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).
plinger and Habermeier, 1995: Brosius and Lps. 1995; Hardy, 2018; Wolfsfeld and Sheafer, 2006). After an event or issue has received extensive cover- age 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 ad- ditional 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 (Hardy, 2018; Wien and 2009).
Given the influence of media storms and other news on democratic debate and public discourse (Walgrave et al., 2017; King et al., 2017), our find- ings also offer a step towards answering the ques- tion 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 anal- yses in the agenda setting and framing space. Our model for predicting article similarity, as well as data and replication code for this paper, are avail- able at https: //g:::///iiii tht.. com/blitt2011 /0mmiittttmmmmmstttrmmss
While the gatekeeping threshold primarily con- cerns within-outlet reporting practices, the inter- media agenda setting hypothesis describes the em- ulation of coverage between outlets. When out- lets see that story has risen to prominence in other outlets, they may be incentivized to shift their own coverage (Boydstun et al., 2014; Hardy, 2018; Vliegenthart and Walgrave, 2008; Anderson, 2010; Vargo and Guo, 2017). This incentivization may be especially true for outlets which feel that their principal reporting domains are being encroached upon (Boydstun et al., 2014).
2 Background
3 Creating Media Storms Corpus
Defining Media Storms A number of definitions have been employed to characterize explosive and long-lasting stories that arise in the news ecosystem (Vasterman, 2018 8). Kepplinger and Habermeier (1995) present the idea of news waves, which are started by key event and trigger inereased cov- erage of similar events. Vasterman (2005) 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 seif-reinforcing process of news production result- ing in an increased number of articles pertaining to single issue. Boydstun et al. (2014)'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", shift- ing from routine coverage to an intensive focus on a particular event, issue, or topic over period of time (Hardy, 2018)-
To investigate media storms at scale, we augment set of three large and diverse datasets by creating model to group news articles into story clusters.
3.1 Data
To capture coverage dynamics from a wide variety of sources, we use the NELA-GT-2020, NELA- GT-2021, and NELA-Local datasets (Gruppi et al., 2021; Hore et al., 2022). NELA-GT-2020 is com- posed of nearly 1.8 million articles published over the course of 2020. Notably, this coverage from 519 sources varies widely in focus, including po- litical, conspiratorial, and health-oriented content alongside coverage from mainstream outlets such as The New York Times and The Washington Post. NELA-GT-2021 has a similar outlet distribution and contains over 1.8 million articles from 367 out- lets in 2021. In addition, this data includes Media Bias/Fact Check ground truth scores for reliable, mixed, and unreliable coverage. We also included local news from the NELA-Local database, which consists of more than 1.4 million articles from 313 outlets in 46 states. Local only spans from April 1, 2020 December 31, 2021, so NELA-
Mechanisms Two primary mechanisms have been identified to explain the development of me- dia storms. First, media storms may be caused by a lowering of the gatekeeping threshold (Kep-

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GT 2020 was truncated to match this range. In all NELA datasets, news was collected from RSS feeds, meaning that our study focuses on online, rather than print media. After merging the three datasets and removing duplicate articles from the same outlet, our data consists of 4.2 million articles from 815 unique outlets.
Model
Mean
Max
Rank
Our Model
0.860
0.861
HFL. EMBEDDIA
0.839
0.872
1
0.704
0.855
2
0.786
0,855
3
WueDevils DataScience-Polim 0.770
0.822
0.857
4
0.873
5
Table 1: Our model compared to the top five models in the English-only portion of the SemEval news compari- task, including those with the top mean (HFL; Xu ct al., 2022) and max MittaScience-Polimee Di Giovanni et al., 2022) Pearson correlation. Our bi-encoding strat- egy is adapted from the design of the WueDevils model (Wangsadirdja et al., 2022), though our refinements lead to better performance. Using the scoring metric from the SemEval competition, our system would rank 2nd of 30, and achieves the overall best mean performance (computed over random seeds).
3.2 News Similarity Model
order to group news articles together into story clusters, we created a model to identify whether two articles are about the same story. Here, we make use of the dataset from the SemEEval task on news article similarity (Chen et al., 2022), which contains labels for 4,950 pairs of news articles rated for similarity. Our goal was create an accurate model that could scale to millions of articles. To do so, we fit a bi-encoder MPNet model initialized with all-mpnet-base 2020). 2 We fine-tuned this model by minimizing mean squared error between the cosine similarity of article pairs embedded by the model and the corresponding human similarity ratings.
English using the Google Translate API, keeping the human similarity ratings for the corresponding non-English article pairs.
parameters (Song et al.,
Our model was trained over 2 epochs with a batch size of 5 and a linear learning rate starting at 2c-6. On the English subset of the SemEval test set, our model achieves near state-of-the-art perfor- mance, as shown in Table 1.3 Appendix A includes additional experiments testing the impact of var- ious modeling choices, and our best berf-perforning model is available online.
Our approach incorporates multiple techniques from models submitted to the SemEval competition (Chen et al., 2022). To ensure scalability, we used a bi-encoder approach; at interference time, this allows us to precompute all n article embeddings (O(n)), rather than using a cross-encoder, (as was used by the top performing model in the compe- tition, Xu al., 2022), which requires O(n?) to encode and evaluate all pairs. The model from Wangsadirdja al. (2022) also used bi-encoder approach but we simplified and streamlined their method for better scalability by discard the ad- ditional convolutional layers and the ensembling strategy used by these authors.
3.3 Creating Story Clusters
To create story clusters, we first embedded each article in our complete news dataset using our fine- tuned article encoder. We then computed the simi- larity of article pairs to identify story clusters. To do so, first reduced the search space of poten- tial article clusters by considering only article pairs published less than eight days apart, with least one named entity common. Named entities that were very common (found in over 20,000 articles) were discarded from this filtering process in order to maintain computational tractability (details in Appendix B).
Inspired by Xu et al. (2022), concatenate the first 288 tokens of each article with the last 96 tokens, in order to include information from both the head and tail of each article. Of the 4,950 article pairs available, 1,791 were English-Engie pairs and the remaining 3,159 were non- won-English. In order to augment this data, (as done by Xu et al., 2022), we translated the non-English articles into
For the remaining article pairs, computed their cosine similarity and kept only the article pairs having a cosine similarity greater than 0.9, based on pilot experiments. The resulting pairwise similarity matrix all article pairs over our threshold forms a graph, and our story clusters are the connected components of this graph.
'The original SemEval training set included 4,918 arti- cles, slightly larger number of article pairs was actually released online and used by other teams the task. The annotations include similarity judgements on seven different dimensions, but we only used the OVER OVERALL similarity judge- ments, which we linearly rescale the range (0. 1].
'Averaging over random seeds, our model achieves the highest mean correlation score, hut would rank second due to how the competition scoring weights multiple entrics.
'https: /hhugginggfaee ace. co/ co/sentence tereennceerrnssseee faansoorman Formers/ all-mpnet -ampppttttbeee22 base-v2

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300
Ruth Bader Ginsberg
Biden Wins 2020 Election
250
Trial of Derek Chauvin
Sentencing of Derek Chauvin
Andrew Cuamo Horassment Scandil
# articles
200
Joe Biden Accused Assualt
Georgia Senate Runoffs
Texas Abertion Ban
Death of John Lewis
150
Trump's impeachment Trial
Manslaughter Trial
100
50
0
May 2020
Jul 2020
Sep 2020
Nov 2020
Jan 2021
Mar 2021
May 2021
Jul1
Sep 2021
Nov 2021
Figure 2: Time series of all 98 media storms from April 2020 December 31, 2021, clustered based our article similarity model ($3.2), Stories are classified as media storms using criteria based on cutoffs for duration and number outlets with a high volume of sustained coverage (see $3.3).
3.4 Identifying Media Storms
Feature
Min
Max
Mean
Median
Number of Articles Duration (days)
31
1378
207.3
156
Our corpus of story cluster labelled news articles lends itself naturally the identification of media storms at the level of events. We adapted Boydstun et al. (2014)'s definition of media storms toward this end. 4 Working with the notions of a high vol- ume of sustained coverage across multiple outlets for a sustained period of time, we operationalize "storm mode" as periods in which an outlet devotes at least 3% of their coverage to story in three day period. Media storms are those stories for which coverage lasts least one week and where least five outlets cover the story in this mode. All articles clustered as part of that story cluster are considered as part of the overall media storm. These criteria yield 98 media storms in our 21 month period. Fig- ure 2 shows the timeline of all storms with example storm descriptions. Brief descriptions of all storms can be found in Table 4 in Appendix D.
7
54
15.0
11
Number of Outlets
5
200
76.1
74
%6 National
0
100
59.1
62
Table 2: Descriptive statistics our 98 media storms
where % National refers to the percentage of articles from national outlets. Summary statistics indicate that media storms identified using our pipeline are inline with expectations from prior literature.
story clusters are selected based on this definition. Our summary statistics media storms serve two ends: first, they confirm that our selection criteria yield storms that are in line with previous theoreti- cal and empirical work on media storms. Second, they demonstrate that previous hypotheses about the quantitative features of news storms have em- pirical support in our data.
4.1 Storm Duration
4 Characterizing Media Storms
As shown in Table 2, our media storms have an av- erage article count of 207.5 and an average duration of 15 days. Prior work has posited that media hy- pes die down after 21 days and, in analyses of arti- cles from two outlets, presented empirical evidence of storms having an average duration of between 14.5 and 16.7 days (Vasterman, 2005; Wien and Elmelund- Prastteerrrr.rrrrrrrr. 2009; Boydstun et al., 20)4). Our average duration of 15 days aligns with these expectations from prior literature and confirms that our cutoffs in storm selection are reasonable.
Our definition and selection process focuses on media outlets reporting in storm mode over a sub- stantial period of time. We now characterize which
'Boydstun et al. (2014) define media storms "an explo- sive increase in news coverage of specific stituting substantial share of the total news agenda [across multiple outlets] during certain time." Because they were relying on hand-coded headline topics to identify storms, and only considered individual outlets, they ignored the "multi- media- ness" part their definition, and operationalized concept as least a 150% increase coverage of issue, lasting at least week, and comprising at least 20% a pa- per's coverage devoted to the issue during that time. Because we identify storms at the level of events, rather than issue areas, we use somewhat different criteria.
item con-
4.2 Storms Multi-Outlet Phenomena
In addition to characterizing storm lengths, prior work has also hypothesized that media storms in-
"Wo exclude three day windows in which an outlet has fewer than 40 stories.

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The Development of Media Storms
Proportion of Stories Terminated by Nth Day
1.0
#f Articiees
0.8
0.6
0.4
of Unique States
0.2
Media Storms
Non Media Storms
0.0
10
20
Day of Story
Figure 4: Empirical CDF of the duration of media storm and non-storm stories. Media storm stories last far longer than the average non-storm story.
Days Since First Article
Figure 3: Average time series for the daily article count (top) and unique US state count (bottom) of media storm coverage. The average storm peaks early then fades slowly from the media. Shaded bands correspond to bootstrapped 95% confidence intervals.
This average series quickly rises to prominence within three days, declines steadily until day 10, and diminishes far more gradually after that. A similar trend is observed for the number of unique US states represented by the outlets involved in storm's coverage (Figure bottom).
volve coverage in many outlets (Boydstun et al., 2014). Given that our storm selection criteria re- quire only five outlets to report in storm mode, it is entirely plausible that only few outlets in niche corner of the media ecosystem could gen- erate storms. Summary data of our media storms (Table 2), however, confirms that this very rarely the case, with the average storm involving 76 out- lets. Furthermore, storms are far less likely to be contained within one part of the media ecosystem than non-storms, with the average storm being com- prised of 59.1 and 40.9 percent national and local coverage respectively.
Temporal statistics of media storms provide sup- port for previous findings that they are both explo- sive and long-lasting. The median day for storm's 's peak coverage is day 4.5, and the most common day for peak coverage is the first day, followed by days 2-5.6 6 In other words, most storms peak almost im- mediately, demonstrating the explosiveness of their coverage. To show how long media storms linger the press, we use the empirical CDF of storm dura- tion, which is presented in Figure 4. Here, we find that the percentage of storms lasting for weeks of coverage is far higher for storms than non-storms. Although most storms have an early peak of coverage, we also identify heterogeneity in media- storm development, with a significant fraction of stories having two distinct peaks or even peaking at the end of their respective time series. These late-peaking stories appear to relate to events that be anticipated.
4.3 The Timeline of Storm Development
The question of how quickly information in media ecosystems rises and falls is the subject of much prior work (e.g., Wien and Elmelund-Prr Prresteddrrasteeeerr, 2009; Aldoory and Grunig, 2012; Castaldo et al., 2022). Time series analyses of political rumors, misinformation, memes, and search keywords have found that attention around given construct dis- plays distinet patterns, typically rising to peak and falling very quickly thereafter (Leskovec et al., 2009; Shin et al., 2018). Media storms, however, have been differentiated as having an explosive rise in coverage followed by relatively smooth, gradual changes (Boydstun et al., 2014).
inherently ongoing
To verify this qualitatively, we perform a manual review of storms peaking on or after day 15. Out of these 13 late-peak storms, eight cover stories with clear anticipated peak event such court cases, elections and bills passing through Congress. The remaining five storms pertain ongoing coverage of COVID-19 and assault allegations made by Tara Reade during Joe Biden's campaign. This aligns
To elucidate the general temporal dynamics of media storms, Figure 3 (top) displays the average volume of coverage over time across all 98 storms.
"The distribution of peak timing and other properties of our media storms are included as plots in Appendix C.

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with work correlating the temporal dynamics of the news with stories pertaining different top- ies (Geiß, 2018), and indicates need for further research to understand how storms with different content develop. Additional plots showing the dis- tribution of storm properties are included in Figure 10 in Appendix C.
lase,law
sill, house- election, stote, trump, pee.pereett house water, climate,, palice,officers trump w.b.b,ddd.... biden,,residentt president vaccine,covie heaidh m media, faccboo covid, wwwonaviirs.caacc....... california, sanbbrder
5 Topical Distribution of Storm Coverage
military,an air,
game, searmessesoongeeee games
school,ss school,students schools
Examining media storms at the level of issues, Boydstun al. (2014) 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 Govern- ment Operations, Defense, International Affairs (which were also the most common issues in all coverage, but to much lesser degree).? This observation also aligns with literature positing that stories with certain news values are more appeal- ing to the media and therefore more likely to trig- ger storm-development mechanisms (Harcup and O' O'Neill, 2017).
people.healthe work a.m.family, p.m store,amazon amazon,home it's it's "'sggame.scason russia, russian, wwt.ruuuniiiiittrry...... miliiary- china, world,chineee team,points, conn.pontts.gme
people wwr.e,
city,county,co council
emm.eeet.year
show, film,music
eng weaedl/eeeeeeedeeeeeeeee
heople,time..amee
million, ream,noney
Storm Topic % Non-Storm Topic
The difference the percentage of articles associated with given topic between articles in me- dia storms and those not media storms. Articles are assigned the topic with the highest proportion in their topic distribution. Media storms involve disproportion- ately more coverage of politics, court cases, policing, and climate.
Figure 5:
To test this theory at the level of media storms tied to specific events, we Latent Dirichlet Anal- ysis (Blei al., 2003) on our entire corpus of news articles using Mallet (McCallum, 2002). We set the number of topics to be 30, infer distribution over topics for each article, and associate each ar- ticle with its highest-scoring topic. Finally, we aggregate these topics across storm and non-storm articles.
Regarding the topic skew of media storms, we hypothesize that this distinction may be result of certain types of events lending themselves to ongoing and follow-up coverage. A topic like tertainment may contain more disparate one-off events, whereas legislation and disasters reflect events whose development and consequences can last for longer periods of time.
As shown in Figure 5 and supplemental Figures 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 distribu- tion for non-storm articles. In addition, we find that certain topics make up much larger fraction of coverage for storms than non-storms. In particu- lar, topics related to politics, natural disasters, and policing are overrepresented in storms, whereas topics related to sports, entertainment, and daily life are underrepresented. contrast Boydstun et al. (2014), however, we find that topies 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 top- ics (see Figure 12 in Appendix C). This may relate in part use of much wider set of media outlets, including many local news sources.
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 lowering of the gate- kceping threshold- increased recognition of the newsworthiness of particular topic. Second, we test whether media storms are associated with in- termedia agenda-setting behavior, a phenomenon wherein outlets mimic one another to keep their coverage relevant.
"Boydstun et al. (2014) 4) used 27 hand-coded issue areas from the Comparative Agendas Project.

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6.1 Gatekeeping
7.5
Gatekceping refers the process of filtering or selecting what information present out of the vast universe of possibilities (Shoemaker and Vos, 2009). One potential explanatory mechanisms for the emergence of media storms a lowering of the gatekeeping threshold. According to this theory. once a particular topic, issue, or event has received a critical mass of coverage, it is seen more news- worthy and will therefore receive even more cov- erage (Boydstun al., 2014). At the news-outlet level, this effect can be explained by disruptions in publishing routines. Indeed, interview data finds that in extreme cases, news outlets may even reas- sign almost every journalist to a particular issue or event (Hardy, 2018).
Average Topie
6.5
6.0
5.5
5.0
storm articles included
storm articles excluded
-10
Day Relative to Storm
Figure 6: Time series depicting the average percentage of each media storm's associated topics articles be- fore and after the storm's 's first day. Each media storm is assigned the topic with the highest percentage when summing over all of its articles. Media storms are pre- ceded by rise in their associated topic and this topic increases significantly once storm begins.
Of interest here is whether surge in attention on single story is correlated with a broader shift in the attention devoted to related stories. According to this framework, once the gatekceping threshold is lowered, events are stitched together by the me- dia to form a broader reporting agenda with even historical events being introduced back into the dis- course (Kepplinger and Habermeier, 1995; Seguin, 2016; Baumgartner and Jones, 1993). It follows that if the gatekeeping threshold mechanism holds for our media storms, they should be accompanied by waves of additional topically related coverage.
tention issues begins rise before the start of associated storms. Some this might accounted for by early articles that were not properly matched to the story cluster, but more compelling expla- nation is that coverage of an issue begins to rise early in anticipation of particular event. Given that topics related to somewhat predictable events, such court cases, are over-represented among storms (see Figure 5), this early rise may represent news organizations "priming the pump" in advance of their coverage of the actual event.
To test this hypothesis empirically, we first iden- tify time period of 14 days before and after the first day of coverage of each storm. We then limit ourselves to only outlets that report the media storm in question. For each day in our 29-day window, we then measure the mean percentage of coverage devoted to the topic associated with cor- responding storm, averaged across all storms. 8
6.2 Intermedia Agenda Setting
A second mechanism proposed explain media storms is inter-media agenda setting (Boydstun et al., 2014; Hardy, 2018). According to this the- ory, reporting from one news outlet signals the im- portance given event or issue to other outlets (Vliegenthart and Walgrave, 2008).
When including all articles, we find an increase of 1.6 percentage points from the mean pre-storm level to the maximum level during the storm (sce Figure 6). If exclude the articles associated with the media storm, and only consider others on the same topic, we find there still an increase of 1.1 percentage points from the mean pre-storm topic level to the maximum post-storm level. Together, these suggest that media storms lead to increased at- tention to the broader issue they are part of, thereby disrupting normal publishing routines.
To determine which outlets lead in storm cov- crage, we analyze the patterns of publishing storms across outlets. To do so, we construct a weighted graph with outlets as nodes. For all out- lets and storms, we find the first article each outlet publishes about a given storm. We then assign credit for influence to all outlets that had an arti- cle related to that storm in the preceding two days. The edge between outlet i and outlet is thus the number of storms in which outlet i published in the two days preceding the first article from outlet j. Using the 20 outlets with the most articles in our 98 storms (among those rated as reliable the NELA data), Figure shows the directed influ-
Perhaps more surprisingly, we also see that at-
*A storm's 's topic is defined as the topic with the highest concentration when summing topic distributions across all of the associated articles.

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Slate
The Hill
New York Times
France
UPI
Vox
U.S. News
PBS
CBS News
BBC
Today
Sky News U.S
NPR
Yahoo News
The Toronto Star
ABC News
National Review
CNBC
Evening Standard
NewSweek
Figure 7: network depiction of influence among the top 20 national news sources rated as reliable our data. Directed edges show amount of influence based timing of reporting storms. Larger nodes have higher net influence (total outgoing minus incoming edge weights). Colors are used only to differentiate sources.
outlets do appear most prominently in this analysis, including nonprofits (PBS, NPR), and digital-only platforms like Yahoo News.
unrellable
Figure 8 presents a similar analysis at the level of different parts of the media ecosystem. Once again, see clear distinction that aligns with an intermedia agenda setting mechanism for storm development. In particular, reliable news appears to be highly influential on local and on less-reliable national news. The opposite is true of unreliable news, which has weak influence relationships to all outlet types. One finding of particular interest is that local news is disproportionately influenced by reliable news but influences mixed and unreli- able news disproportionately. The discovery of this heterogeneity in influence signals need for more research on the relationship between local coverage and national coverage of varying types.
reliable
local
mixed
Figure 8: A network depiction of influence between all local outlets and national outlets having three different factuality ratings (reliable, unreliable, mixed). Only the top 200 outlets in terms of media storm coverage considered. Edge weights correspond to the amount of influence between different types of outlets, and node sizes represent net influence, as in Figure
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 origi- nal paper, Boydstun et al. (2014) find that Google queries corresponding to given event spike more during media storms than during coverage of simi- lar non-storm stories. Furthermore Walgrave et al. (2017) find that news stories exert stronger in- fluence over the congressional agenda when these
ence among these outlets, with node size mapping to the net influence of that outlet, considering all incoming and outgoing edges. This graph is con- sistent with the hypothesis that intermedia agenda setting present as storms migrate through the me- dia ecosystem. Overall, we find a rich network of connections, with no single outlet consistently pub- lishing in advance of all others. Nevertheless, a few

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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.
connect these results with the literature on framing and agenda setting. Our analysis finds that unre- liable 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 main- stream, revealing the "truth" that the mainstream is "unwilling" to say (Andersen et al., 2023; Holt et al., 2019). 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.
Although our analysis of media storms revealed a somewhat different distribution of topics than the hand-coded analysis of news headlines in Boyd- stun et al. (2014), 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.
Finally, future work may address the limitations of our news similarity model and article match- ing 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 iter- ation would reliably distinguish between articles about different events in spite of their high textual similarity.
While some storms, (such as the trial of Derek Chauvin), peak weeks after they begin, the typi- cal 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 some- times 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 mech- anisms governing news production, with limited resources available within each news outlet.
Conclusion
News organizations shape the public perception of what issues most salient and media storms play an important role in saturating coverage 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-thhe-an model to identify news articles on the same event.
single
Beyond the results in this paper, the methodol- ogy, model, and dataset we contribute open the door for future research on media storms. One impor- tant 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 con- ditions which cause stories to gain traction. Such 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 ex- periments to study the media conditions in which storm are most likely to develop. and could be com- pared 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.
Using the 98 storms in our data, we show how they migrate through the media ecosystem via struc- tured 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 tem- poral and topical characteristics. Furthermore, they indicate that some news outlets and categories of news 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.
Acknowledgements
We would like think Amber Boydstun, Jill Laufer, and anonymous reviewers for their help- ful feedback and suggestions.
In addition to the estimation of larger and more diverse diffusion networks, future work could also

---

Limitations
sess what causes media storms. Given the self- referential nature of the news production process (Kepplinger and Habermeier, 1995), these causal mechanisms are incredibly difficult to locate. As such, large scale descriptive results still represent an important step in extending the current state of the literature.
Our study uses state-of-the-ard modeling to present large scale empirical investigation of media storms across many outlets with diverse audiences. However, there are a number of notable limitations to our approach as well as directions for future research.
One initial limitation in our approach comes from our reliance three existing datasets of news articles. These datasets only cover a 21 month overlapping period and include variety of outlets, some of which are based outside the United States. Given this composition outlets, our data is likely not representative of any one news ecosystem but rather captures a broad sample of the U.S. national and local media outlets with some international coverage included.
References
Linda Aldoory and James E Grunig. 2012. The rise and fall of hot-issue publics: Relationships that develop from media coverage of events and crises. Interna- tional Journal of Strategic Communication, 6(1):93- 108.
Kim Andersen, Adam Shehata, and Dennis Andersson. 2023. Alternative news orientation and trust in main- stream media: A longitudinal audience perspective. Digital Journalism, 11(5):833-83 852.
In addition, our three news datasets were origi- nally collected from RSS feeds, meaning that we have limited knowledge on the completeness of coverage. While RSS feeds typically mirror the online feed of news organizations, outlets which push only fraction of coverage to their feed may interfere with the accuracy of our results. Despite these potential issues, we find that the duration and temporal patterns of our identified media storms align with what is expected given prior literature.
C. W. Anderson. 2010. Journalistic networks and the diffusion of local news: The brief, happy news life of the "Francisville Four". Politicul Communication, 27(3):289-300
Frank R. Baumgartner and Bryan D. Jones. 1993. Agen- das and instability in American politics. University of Chicago Press.
David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent Dirichlet allocation. J. Mach. Learn. Rex,, 3:993-1022.
In terms of identifying storms, one limitation in our work is the scalability of our clustering pipeline. To avoid computing pairwise similarity among n² pairs for millions of articles, our current approach considers pairwise similarity between all articles with named entity pairs having under 20,000 oc- currences, restricted to those article pairs that are published less than eight days apart. While larger events can be identified through links between pairs of articles with different, less common named en- tities, our cutoff of 20,000 might unintentionally exclude some of the largest news storms. Most of the entities that were removed correspond to promi- nent politicians or organizations (e.g.. Joe Biden, the FBI, etc.). However, one notable exception is that our dataset does not include a media storm specifically about the death of George Floyd, as this name was mentioned in more than 80,000 arti- cles in our full dataset, and was therefore removed before forming story clusters.
Amber E. Boydstun, Anne Hardy, and Stefaan Walgrave. 2014. Two faces of media attention: Media storm ver- sus non-storm coverage. Political Communication. 31 31(4):509-31.
Hans-Bere Brosius and Peter Eps. 1995. Prototyp- ing through key events: News selection in the case of violence against aliens and asylum seekers in Germany. European Journal of Communication, 10(3):391-41..
Maria Castaldo, Tommaso Venturini, Paolo Frasca, and Floriana Gargiulo. 2022. Junk news bubbles mod- elling the rise and fall of attention in online arenas. new media & society, 2409):2077
Xi Chen, Ali Zeynali, Chico Camargo. Fabian Flöck. Devin Gaffney, Przemyslaw Grabowicz, Scott Hale, David Jurgens, and Mattia Samory. 2022. SemEval- 2022 task 8: Multilingual news article similarity. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemFval-20022, pages 1094- 1106.
Marco Di Giovanni, Thomas Tasca, and Marco Bram- billa. 2022. DataScience-Poliin
When it comes to analyzing our identified me- dia storms, our evidence has important limitations as well. Our results on the development of me- dia storms are merely correlational and fail to as-
SemEval-2022 Task 8: Stacking language models to predict news article similarity. In Proceedings of the 16th Interna- tional Workshop on Semantic Evaluation (SemEval- 2022), pages 1229-1234.

---

Stefan Geiß. 2018. The dynamics of media attention to issues: Towards standardizing measures, dimensions, and profiles. In From Media Hype to Twitter Storm, pages 83-1 14, Amsterdam University Press.
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie- Yan Liu. 2020. MPNet: Masked and permuted pre- training for language understanding. In Advances in Neural Information Processing Systems, pages 16857-16867.
Maurício Gruppi, Benjamin D. Horne, and Sibel Adali. 2021. NELA-GT-20200 A large multi-labelled news dataset for the study of misinformation in news arti- cles. CoRR, abs/2 102.2200..
Chris J. Vargo and Lei Guo. 2017. Networks, big data, and intermedia agenda setting: An analysis of traditional, partisan, and emerging online U.S. news. Journalism & Mass Communication Quar- terly, 94(4): 1031-1055.
Tony Hareup and Deirdre O'Neill. 2017. What news? Journalism Studies, 18( (6(1))14770 148888888-88
Peter Vasterman. 2018. Introduction. In Peter Vaster- man, editor, From Media Hype to Twitter Storm, pages 17-36. Amsterdam University Press.
Anne Hardy. 2018. The mechanisms of media storms. Peter Vasterman, editor, From Media Hype to Twit- ter Storm, pages 133-148. Amsterdam University Press.
Peter L.M. Vasterman. 2005.
Media-hype: Self- reinforcing news waves, journalistic standards and the construction social problems. European Jour- nol of Communication, 20(4):508-530.
Kristoffer Holt, Tine Ustad Figenschou, and Lena Frischlich. 2019. Key dimensions alternative news media. Digital Journalism, 7(7):860 869.
Rens Vliegenthart and Stefaan Walgrave. 2008 The contingency of intermedia agenda setting: A longitu- dinal study in Belgium. Journalism & Mass Commu- nication Quarterly, 85(4):800-877.
Benjamin D. Horne, Maurício Gruppi. Kenneth Joseph, Jon Green, John Wihbey, and Sibel Adalı. 2022. NELA-Local: A dataset of U.S. local news articles for the study of county-level news ecosystems. CoRR. 0bs/2203.08600)
Stefaan Walgrave, Amber E. Boydstun, Rens Vliegen- thart, and Anne Hardy. 2017. The nonlinear effect of information on political attention: Media storms and U.S. congressional hearings. Political Communica- tion, 34(4):548 570.
Hans Mathias Kepplinger and Johanna Habermeier. 1995. The impact of key events on the presenta- tion of reality. European Journal of Communication, 10(3)33713390.
Dirk Wangsadirdja, Felix Heinickel, Simon Trapp. Al- bin Zehe, Konstantin Kobs, and Andreas Hotho. 2022, WueDevils at SemEval-2022 Task 8: Multilingual news article similarity via pair-wise sentence similar- ity matrices. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1235-1243.
Gary King, Benjamin Schneer, and Ariel White. 2017. How the news media activate public expression and influence national agendas. Science, 358:776-780.
Ana Ines Langer and Johannes Gruber. 2021. Politi- cal agenda setting in the hybrid media system: Why legacy media still matter great deal. The Interna- tional Journal of Press/Politics, 26(2):3133340.
Charlotte Wien and Christian Hamellnd--Prestekenn 2009. An anatomy media hypes: Developing model for the dynamics and structure of intense media coverage of single issues. European Journal of Communication,
Jure Leskovec, Lars Backstrom, and Jon Kleinberg. 2009. Meme-tracking and the dynamics of the news cycle. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, page 497-506.
Gadi Wolfsfeld and Tamir Sheafer. 2006. Competing actors and the construction of political news: The contest over waves in Israel. Political Communica- tion, 23(3):333333)
Andrew McCallum. 2002. MALLET: A machine learn- ing for language toolkit. http://mallet.cs http:/maalet.cs.umass.ek umass.edu.
Maxwell E. McCombs and Donald L. Shaw. 1972. The agenda-setting function of mass media. The Public Opinion Quarterly, 3662):176-187.
Zihang Xu, Ziqing Yang. Yiming Cui, and Zhigang Chen. 2022. HFL SemEval-2022 task 8: A fingussics--nsspee regression model with data aug- mentation for multilingual news similarity. In Pro- ceedings of the 16th International Workshop Semantic Evaluation HomeEval-2022) pages 1114 1120.
Charles Seguin. 2016. Cascades of coverage: Dynamics of media attention to social movement organizations. Social Forces, 94(3):997-0000.
Jieun Shin, Lian Jian, Kevin Driscoll, and François Bar. 2018. The diffusion of misinformation on so- cial media: Temporal pattern, message, and source. Computers Human Behavior, 83:278-287
Pamela Shoemaker and Tim P. Vos. 2009. Gatekeep- ing Theory. Routledge.

---

A News Similarity Modeling Details
Head
Tail
Translated
Mean
Max
S.D.
288
yes 0.885
0.898
0.017
192
192
yes 0.886
0.894
0.006
The SemEval task on news article similarity (Chen et al., 2022) was multilingual challenge, where teams were given access pairs of news articles rated in terms of multiple types of similarity (e.g.. temporal, geographic, tone) on scale of to We relied on the "overall" similarity judgements, and rescaled the similarity scores to [0, 1].
384
yes 0.885
0.900
0.008
288
96
no 0.884
0.904
0.015
Table 3: Ablation results showing Pearson correlation coefficients over five-fold cross validation. Translated refers to the inclusion of the back translation of non- English articles. Head and Tail refers the number of tokens concatenated from the head and tail of arti- cles, respectively. Translated article pairs always excluded from evaluation.
The top-performing model in the competition (Xu et al., 2022) used cross-encoder, in which model was trained using pairs of article texts given to the model simultaneously. Because such an ap- proach does not scale to rating the similarity mil- lions of articles, we opted to use a more efficient bi-encoder (as used by other submissions, such as Wangsadirdja et al., 2022), in which model is trained to usefully embed articles, such that the similarity of embeddings can then be quickly com- puted.
of other types were more likely to be associated with an unreasonably large number of articles. For computational reasons, entities with over 20,000 associated news articles were also removed. This resulted in the exclusion of 113 entities, which left 2,614, 176 distinet entities remaining with at least two associated articles. Finally, we created an inverted index between each entity and its cor- responding articles. This index was used to deter- mine which pairs of articles had at least one entity in common and were thus designated as eligible for comparison using our pairwise similarity model. While entities such as "FDA", "Biden", and "Trump" were among the removed entities, we found multiple media storms with coverage relat- ing to these entities and mentioning them in the article text. This suggests that many media storms are identifiable with entities which are less com- mon across all articles and more specific to the associated event. For completeness, we include the full list of excluded entities in the replication code associated with this project.
In developing our model, we performed an abla- tion study to evaluate the effectiveness of various data augmentation decisions. Taking inspiration from (Xu et al., 2022)'s approach, we experimented with translation of training data into English as well various concatenation of the head and tail of articles. Five-fold cross validation was used evaluate the efficacy of these approaches, with- out making any use of the test data. When using translated data, only the English article pairs from a given fold were used for evaluation.
As shown in Table 3, both methods appear to have a minimal effect on the performance of our model. Despite this null result, we still use trans- lated data and a combination of 288 head tokens and 96 tail tokens. This choice is made because both augmentation strategies were effective in Xu et al. (2022), which was the top-ranked model when evaluated on the SemEval English-only test set.
When embedding articles using our similarity model, we merged the first 288 tokens each ar- ticle including the title with the last 96 tokens, as discussed in Appendix A. As shown in Figure 9 this resulted in the majority of articles being trun- cated. This effect was only modest, however, with the median article length being reduced from 467 words 317 words when identifying words by splitting on whitespace in the article text. We also experimented with models having longer context windows but found that this produced inferior re- sults in pilot experiments.
B Details of Article Clustering
In order to reduce the number of pairwise arti- cle similarity comparisons needed augment our dataset of over 4 million articles, we first applicd a named entity filter. Named entities were extracted from each article using the named entity recogni- tion pipeline in spaCy's wow_ecrreeeeeecceeeee model."
After identifying these entities, we kept only Organization.
C Additional Details on Characterizing Media Storms
those entities corresponding Event, Person, Work of Art, or Product, as entities
Figure 10 shows some additional plots describing the distribution of media storms, in terms of total
/tstps://spacy...o io/models/en

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Document Length: Pre Versus Post Truncation
1.0
0.8
Proportion
Preffruncation
Truncactttioo
500
1000
1500
2000
2500
3000
Number of Words
Figure 9: The empirical CDF plots for the lengths of articles before and after truncation. Number words determined by splitting articles whitespace.
Distribution of Storm Article Count
20
Count
10-
article count, peak day of storm coverage, storm duration in days, and percentage of coverage in national outlets (as opposed to local). Most storms are primarily national, but with small number that are almost entirely local. Similarly, there is a long tail of storms in terms of duration, peak day, and total volume, with the largest storms comprising over 1,000 articles.
200
400
600
800
1000
1200
1400
Articles
Distribution Storm Peak Days
Count
20-
10
0
10
20
30
40
Figures 11 and 12 show the individual average topic distributions for both storms and non-storms as a complement to Figure in the main paper. As can be seen, the distribution of storm topics is more skewed, and shows considerable difference in relative topic rankings.
Peak Day Storm
Distribution Storm Duration
30
Count
20
10
20
30
40
50
D Media Storm Descriptions
Storm Duration
Distribution Storm National Coverage
A complete list of the media storms found in this work is given in Table 4, which includes start date, date of peak coverage (in terms of article count), duration, total number of articles, percent of cov- erage that was from national sources, and brief summary description of the storm.
Count
10.
20
40
60
80
100
Storm National Coverage
Figure 10: Summary distributions for media storms.

---

Storm Topic Distribution
Non-Storm Topic Distribution
court,case,law police, officers,man
police, officers,man wwwid.coronairrus cases people, tiime,familn, mealion.year,me money trump,biden. president
bill,state,honr house
election,stat trump.pres/nn crmnp.presidenthhuur cemmpbidden.rrsiideeee
show, show,film.men music russia, russian, military
water, climate,
people, world, black
cases
engl and, league, ue, london
wague,london
vaccine, coviid, hea,hhalthh
v vaccine, covid,
media, news, www.neewcwccbo..o. facebook
climate,
military
p. w.m,event,yer
world, show,film. www.ffilmmmssst. a. ma.family.p.uu military, air, war people. work school,students,schol schools game.season, team, team,points,ges points,game geople,time,fee england,/eague, london million.year, money china, ccineec
court,case, law
city,county council county,councii
election,ster sttate, voters team, points,game stll,state, trump.presice
www.peeeeettttoooooo house
china, world, chinne,e
a.m, amm.famlly...m geople,health.e it's, seseaoo school.students.schooi
military,air, air,
p.m.event.yea. sttore,amazn,,hom city, www.oooonnncunnnn california,san, borde it's, tes.gon
store.amazon,home
body
semme,season.gameee sillfomia.san.borce die,der,un
5
10
15
10 Topic %
15
Topic %
Figure 11:
Topic distributions for all articles in storm coverage. Each article is assigned to the topic with the highest percentage its topic distribution.
Figure 12: Topic distributions for all articles non- storm coverage. Each article is assigned to the topic with the highest percentage its topic distribution.

---

Length (days)
Article
%
Start date
Peak date
Description
Nat'l
Apr 05, 20
Apr 06,
91
28.6
Boris Johnson's
and reeovery from COVID-
Apr 06, 20
Apr 23,
19
0.0
Minnesota Gov. gives COVID-19 update
Apr 12, 20
May 01,
34
81.8
Joe Biden accused sexual assault
Apr 25, 20
May 06,
18
109
$2.3
Dallas salon owner jailed for violating COVID restrictions
Apr 28, 20
Apr 28,
114
77.2
Mike Pence criticized not wearing mask to Mayo Clinic
May 05, 20
May 07,
57
100.0
Adam Schiff and "Russiagate" transcripts
May 18, 20
May 19,
11
65.5
Response to Trump taking hydroxychloroqqine
May 27, 20
03,
16
251
53.0
Court trials officers in George Floyd's murder
Jun 01, 20
Jul 12, 20
48
90
1.1
Decline and subsequent rise of COVID cases in Minnesota Minneapolis bans police chokcholds in response to George
Jun 01, 20
Jun 05, 20
10
89
22.5
Floyd's murder
Jun 04, 20
Jun 08, 20
14
162
78.4
Support for defunding. abolishing police in Minneapolis
Jun 06, 20
Jun 10, 20
12
144
70.8
Renaming of army bases named after Confederate leaders
Jun 07, 20
Jun 20, 20
17
236
78.8
Trump attempts to prevent Bolton from publishing his memoir
Jun 12, 20
Jun 12, 20
10
103
0.0
COVID-19 updates in Minnesota, North Dakota
Jun 18, 20
Jun 28, 20
15
189
71.4
Mississippi to remove Confederate emblem from flag
Jun 19, 20
Jun 20, 20
148
67.6
Top Manhattan prosecutor fired by Trump
Jun 21, 20
Jun 22, 20
177
74.6
Bubba Wallace finds noose in garage; subsequent coverage
Jul 01, 20
Jul 08, 20
93
55.9
Trump pushes states to reopen their schonls
Jul 08, 20
Jul 13, 20
193
73.1
Death of "Glee" actress Naya Rivera
Jul 10, 20
Jul 11, 20
222
82.4
Trump commutes Roger Stone's 's sentence; ensuing backlash
Jul 13, 20
Jul 15, 20
17
132
41.7
Major retailers update mask policies
Jul 29, 20
Aug 01, 20
20
316
77.8
Trumps says he will ban TikTok
Aug 09, 20
Aug 11, 20
233
59.7
Joc Biden selects Kamala Harris running mate
Aug 15, 20
Aug 18, 20
12
363
43.5
Postmaster general's 's involvment in mail-in voting controversy
Sep 09, 20
Sep 14, 20
81
74.1
TikTok's 's owner partners with Oracle rather than Microsoft
Controversy over presidential debate after Trump contracts
Sep 17, 20
Oct 08,
36
442
64.3
COVID-19
Ruth Bader Ginsburg dies; Trump picks Amy Coney Barrett replace
Sep 18, 20
Sep 19, 20
14
1026
72.1
Oct 04, 20
Oct 07, 20
188
42.0
Vice picepdresieltial debate 2020
Oct 05, 20
Oct 07, 20
155
61.3
Hurricane Delta
Controversy surrounding potential for Biden pack the
Oct 08, 20
Oct 16, 20
15
100
98.0
Supreme Court
Oct 10, 20
Oct
19
622
62.4
Amy Coney Barrett's confirmation hearings and confirmation Controversy surrounding extension hallot counting period
Oct 27, 20
Oct 30, 20
81
29.6
Minnesota
03, 20
Nov
22
45.9
Biden wins 2020 election
15, 20
Nov 22,
16
77.6
Trump's challenge 2020 election results Pennsylvania
Nov 17, 20
Nov 19,
85.1
Michigan certifies election results, sealing Biden's win
27, 20
Nov 30,
107
76.6
Biden wins Wisconsin recount
SCOTUS rejects Trump and Texas's attempt overturn election results
Dec 08, 20
Dee 12, 20
131
59.5
Dec 09, 20
Dec 15,
12
149
66.4
Electoral College casts votes
Dee 31, 20
Jan 04,
10
146
84.9
US request to extradite Julian Assange is blocked
Jan 07, 21
Jan 13, 21
184
73.9
Impeachment attempt after January
Jan 08, 21
Jan 10,
212
88.7
Parler removed from app store
Feb 01, 21
Feb 05, 21
109
72.5
Marjorie Taylor Green ousted from her committees
Feb 02, 21
Feb 03, 21
49
57
7.0
Coronavirus updates in California; Deaths decline
Feb 03, 21
Feb 17, 21
23
64
7.8
Horoscopes referencing celebrities
Feb 08, 21
Feb 13, 21
152
44.7
Trump's 's second impeachment trial
Feb 24, 21
Mar (1, 21
23
525
88.0
Andrew Cuomo accused of harassment
Mar 04,22 21
Apr 20, 21
54
1378
50.0
Trial of Derek Chauvin
Mar 24, 21
Mar 29, 21
107
48.6
Cargo ship blocks Suez Canal
Mar 29, 21
Apr 06, 21
16
102
84.3
Arkansas bans trans healthcare for youth
Apr 13, 21
Apr 13, 21
15
220
60.9
Concern over blood clots after Johnson and Johnson vaccine
May 01, 21
May 12, 21
16
198
85.9
Liz Cheney ousted from house leadership role
May 07, 21
Jun 04, 21
42
391
75.4
Biden's 's infrastructure bill
May 10, 21
May 12, 21
138
67.4
Hacking interferes with Colonial Pipeline
May 30, 21
Jun 13, 21
17
148
65.5
Netanyahu ousted by Isreali coalition
Jun 11,
Jun 16, 21
100
70.0
Biden and Putin meet in Geneva
Jun 14, 21
Jun 17, 21
11
193
72.0
Biden, senate make Juneteenth federal holiday
Jun 20, 21
Jun 25, 21
10
210
56.2
Derck Chauvin Sentenced
Jun 23,
Jun 28, 21
106
56.6
Extreme heatwave hits Pacific Northwest
Jun 23,
Jun 24, 21
240
69.6
Deal reached on Biden's 's infrastructure bill
Jun 25,
Jul 21
11
176
81.8
Trump organization charged with tax crimes
Jul 01,
Jul 05, 21
156
62.2
Tropical Storm Elsa
Jul 05,
Jul 07, 21
205
49.3
Miami death toll climbs after condo collapses

---

Jul 13, 21
Aug 10,
28
62.1
Infrastructure passes through senate
Jul
Jul 16,
10
94.3
Facebook Biden clash over COVID- misinformation
29,
Aug 03,
12
Biden seeks extension for eviction moratorium
Aug 03, 21
Aug 05,
Massive California wildfire
Aug 03,
Aug 03,
16
Cuomo resigns to sexual harassment allegations
Aug 05, 21
Aug 08,
19
70.3
Wildfires in Greece
Texas, Florida schools clash with governments over mask
Aug 09, 21
Aug 27, 21
41
344
87.8
mandates
Aug 18, 21
Aug 24,
65.2
Controversy over Biden's Afghanistan withdrawal deadline
Aug 20, 21
Sep 17, 21
29
82
0.0
COVID-19 updates in Minnesota
Aug 23, 21
Aug 31, 21
16
225
60.9
Wildfires approach Lake Tahoe
Aug 26, 21
Aug 29,
289
60.2
Hurricane Ida
Aug 27, 21
Sep 15, 21
22
142
72.5
Gavin Newsome wins recall ellection
Controversy over General Mark Milley's s communication with
Sep 14, 21
Sep 15, 21
11
127
91.3
China
Sep 14, 21
Sep 17, 21
10
111
66.7
Wildfires threaten to destroy California's sequoias
Sep 16, 21
Sep 17, 21
13
198
62.6
Official bodies approve COVID booster
Sep 20, 21
Sep 20, 21
19
79
0.0
Minnesota COVID-I 19 updates
Sep 30, 21
Nov 03, 21
37
132
90.2
Tight governer race in Virginia
Sep 30, 21
Oct 07, 21
29
332
76.8
Controversy, political response to Texas abortion law
Oct 08, 21
Oct 21, 21
15
209
77.5
House votes hold Steve Bannon in contempt over Jan.
Oct 10, 21
Oct 17, 21
54
13.0
Chicago Sky wins first WNBA title
Oct 12, 21
Oct 20, 21
16
217
54.4
Official bodies approve mixing COVID vaccines and boosters
Oct 20, 21
Nov 02, 21
16
242
52.1
Official bodies approve COVID vaccine for children 5-11
Oct 21, 21
Oct 22, 21
184
67.4
Alec Baldwin kills Halyna Hutchins on
Oct 30, 21
Nov 11, 21
14
107
72.9
Judge refuses Trump's request to block Jan. records
Nov 10, 21
Nov 16, 21
10
126
46.8
Court case of Kylc Rittenhouse
Nov 18, 21
Nov 23, 21
51
66.7
"Unite the Right" trial developments and verdict
Nov 25, 21
Nov 26,
64
81.2
Lauren Boebert makes anti-Muslin comments, apologizes
Nov 28, 21
Dec 23, 21
29
369
58.0
Duante Wright manslaughter trial
Nov 29, 21
Dec 05,
10
337
83.7
CNN fires Chris Cuomo for helping brother
Dec 05,
Dec 05,
73.6
Death of Bob Dole
Dec 05, 21
Dec 16,
19
62
19.4
Horoscopes featuring celebrity names
Dec 07,
Dec 14,
10
75.9
House votes hold Mark Mcadows contempt
Dec
Dec 11,
64.5
Deadly tornadoes Kentucky, southeastern US
Dec
Dec 20,
54.5
Coverage Omicron variant
Dec 23,
Dec
81
35.8
CDC shortens COVID isolation window
Dec 23, 21
Dec 24,
60.8
Airlines cancel flights due COVID staffing shortages
Table 4: Descriptions of all 98 media storms, sorted in chronological order by start date.


Reviews:
[{"review_id": "mRJciXj37D", "reviewer": "Reviewer_iVoD", "summary": "", "questions": "1. Table 2 shows huge differences among the storms properties. It would be very beneficial for your article to explore more subclusters among these storm types and give us better understanding of the different types of storms and their properties. Figure 5 is a step towards it, but it was hard for me to understand what is x-axis in Figure 5 and what do red and blue colors represent.", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your helpful comments and suggestions. We appreciate you noting the amount of work we have put into understanding media storms, and our inclusion of a detailed recipe for extracting them, which underscores the soundness and reproducibility of our work. \n\nIn your review, you raise concerns about lack of novelty in terms of methods and findings; respectfully, we would like to challenge both of these points. Regarding methods, it is true that we borrow and combine ideas from past work on predicting article similarity. However, existing methods (which we compare against), are excessively computationally demanding, and were not designed to be applied at scale. The model we have created (and will release for others to use), allows for highly scalable pairwise comparisons (which we apply to millions of articles in this paper), while nevertheless achieving performance on par with the best of existing systems.\nWith respect to our results and conclusions, many of the findings we present were only theorized in prior literature, without being experimentally tested—particularly the theories on intermedia agenda setting and gatekeeping. Our results provide the first large-scale empirical evidence for these hypotheses, which is a novel contribution to the literature on media storms. \n\nRegarding your concern that “many conclusions are just one of the possible explanations”, we agree about the importance of this. However, establishing causality is challenging, as there are multiple mechanisms at play and no data to support testing each individual. Thus, fully quantifying the causal mechanisms is beyond the scope of our paper. Nevertheless, the results we present here, along with our characterization of media storms, are an important first step in that direction by showing the media storms effect is real. As discussed on lines 502-510, determining causal mechanisms is an important future direction that requires an experimental design (matching, natural experiment, etc…) taking causality into account. We hope to pursue this line of work in a future paper now that we have confirmed the effect.\n\n\nAn additional suggestion was to explore “different types of storms and their properties”. While we agree that there is more we could do in this direction, we refer you to lines 331-338, which focus on the subset of storms which reach peak coverage at the end of the storm rather than the beginning. Here, we find that late-peaking storms appear to cover issues such as court cases, elections, and COVID-19. \n\t\n\nFinally, we greatly appreciate your technical notes on Figure 5 and Table 1. Your confusion about Figure 5 is a result of a plotting error \non our part, which resulted in a miscalibrated x-axis. In the corrected figure, (in which 0 occurs at what is currently marked -2), it is obvious that red and blue refer to those differences which are greater and less than zero, respectively. We will fix this figure in revisions.\nIn the case of Table 1, we report the performance of our model, along with relevant models from the SemEval task. To clarify, we did not accidentally omit a model, as you suppose; as explained in the Table caption, we included HFL for having the highest mean score, DataScience-Polimi for having the highest max score, and WueDevils because we make use of their bi-encoding strategy in our model. We will further clarify this in the updated caption.\n\nBoydstun, A. E., Hardy, A., & Walgrave, S. (2014). Two Faces of Media Attention: Media Storm Versus Non-Storm Coverage. Political Communication, 31(4), 509–531. https://doi.org/10.1080/10584609.2013.875967", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. There is a lot of work done on understanding media storms which agrees with existing knowledge of media storms\n2. There is a detailed recipe on how to extract the storms from the news articles", "reasons_to_reject": "1. There is no novel method\n2. There is no novel conclusion or observation\n3. Many conclusions are just one of the possible explanations - I suggest authors to focus on finding experiments to show that conclusion they think is more likely is the right conclusion", "questions_to_authors": "1. Table 2 shows huge differences among the storms properties. It would be very beneficial for your article to explore more subclusters among these storm types and give us better understanding of the different types of storms and their properties. Figure 5 is a step towards it, but it was hard for me to understand what is x-axis in Figure 5 and what do red and blue colors represent.", "paper_topic_and_main_contributions": "Authors combine NELA-GT-2020,  NELA-GT-2021 and NELA-Local datasets and extract 98 media storms from those. They define media storm as shifting from routine. coverage to an intensive focus on a particular event, issue, or topic over a period of time. Using this dataset, they create News Similarity model using bi-encoder MPNet which achieves the best mean Pearson correlation, but second best max Pearson correlation. They create event clusters by using time, NER, and cosine similarity. Then, they define rules to extract media storms and then evaluate different properties of those storms and show they agree with what is already know in the literature.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "- Table 1 has ranks 1, 2, 4, 5. Looks like authors excluded some comparison model and forgot to update the numbers.\n\n- Figure 5 should be redone - it's hard to understand what is the meaning of the colors and of the X-axis."}}, {"review_id": "Lume5E3gtr", "reviewer": "Reviewer_36nG", "summary": "", "questions": "A. Why was \"3% coverage in a 3 day period\" chosen as the margin for \"media storms\", was there any basis for this selection? Further elaboration on this would be helpful in replicating the research across further data sources.\n\nB. An interesting followup/addition here would be to see how topics evolve from one to another and if media coverage can follow similar trends? Have the authors explored this avenue? ", "limitations": "", "rating": 3, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thoughtful comments and suggestions. We are greatly encouraged by your appreciation of our “in-depth quantitative and qualitative analyses” in addition to “good, improved baselines” for research into news outbursts. \n\nRegarding our choice to only make use of the first 288 and last 96 tokens of news articles, it is true that this does result in the loss of some information. However, the widespread use of the “Inverted pyramid” in journalism means that the “Who, What, When, Where, and Why” of an article are answered within the first few sentences in most cases (Pöttker, 2003). Furthermore, we incorporate article headlines, in addition to body text, which helps ensure that the most important information is included. Finally, the tradeoff of which parts of an article to use was explored by Xu et al., 2022, who achieved best results when keeping most of the head of the article, with some of the tail.\n\nWe also appreciate your concern that a change in our article similarity threshold could alter our results. On this note, we reiterate our finding that the average storm duration of 15 days aligns with prior literature, which gives us confidence that our choices in storm selection are reasonable (lines 271-273). We acknowledge that 0.9 is a high threshold, but any such threshold represents a tradeoff between precision and recall. We emphasize precision because the inclusion of many article clusters which are not about the same event is more likely to muddle the behavior of media storms than analysis of a set of articles which have high similarity but may be incomplete. \n\nRegarding our criteria which requires 3% of coverage in a 3 day period, we acknowledge that different choices might have led to slightly different findings. However, preliminary analyses of the distribution of coverage suggested there is a natural break between stories which are given less than this amount of coverage and those that are given more. Indeed, prior work suggests that we would expect somewhat of a binary distinction in article coverage, based on underlying newsroom mechanisms (Hardy, 2018). We would also like to reemphasize that we will release all code and data required to recreate our analyses, which will enable others to explore the effect of varying choices with respect to such thresholds, including those above.\n\nFinally, we love your idea to explore “how topics evolve from one to another and if media coverage follows similar trends”. We appreciate the suggestion of this exciting research direction and underscore that the provision of our dataset, model, and methodology allow for scholars with a range of backgrounds and expertise to pursue questions such as these in future work.\n\nPöttker, H. (2003). News and its communicative quality: The inverted pyramid—when and why did it appear? Journalism Studies, 4(4), 501–511. https://doi.org/10.1080/1461670032000136596\n\nXu, Z., Yang, Z., Cui, Y., & Chen, Z. (2022). HFL at SemEval-2022 Task 8: A Linguistics-inspired Regression Model with Data Augmentation for Multilingual News Similarity. Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), 1114–1120. https://doi.org/10.18653/v1/2022.semeval-1.157\n\nHardy, H. (2018). 5. the mechanisms of media storms. In Peter Vasterman (Ed.), From Media Hype to Twitter Storm (pp. 133–148). Amsterdam University Press.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "A. In-depth qualitative and quantitative analysis of media storms, what they are, their implications and how they come into being. \nB. While popularity clusters and temporal evolution have been studied in depth with respect to social media and news coverage, outbursts are still significant aspects to study for understanding significance of events. This study, dataset and modeling approach have the potential to serve as good, improved baselines for research of this kind.", "reasons_to_reject": "A. By selecting the first 288 tokens and the last 96 tokens, how are the authors ensuring the true semantic meaning of the articles is being covered rather than context and linguistic fluff use to build up a news article and conclude it respectively? A lot of articles consist of a premise and conclusion which can possibly consist of other related events or anecdotal pieces etc, that might not be relevant to the exact topic at hand. Whereas, some articles are to the point. When trying to pair such articles with a VERY high threshold of 0.9, there is a strong possibility of missed correlations and missed article linkup, which can possibly alter results (even if slightly). It'll be good to have some more depth about the data in terms of average length of articles when data is truncated etc., and how these scenarios are being handled in the study.", "questions_to_authors": "A. Why was \"3% coverage in a 3 day period\" chosen as the margin for \"media storms\", was there any basis for this selection? Further elaboration on this would be helpful in replicating the research across further data sources.\n\nB. An interesting followup/addition here would be to see how topics evolve from one to another and if media coverage can follow similar trends? Have the authors explored this avenue? ", "paper_topic_and_main_contributions": "The paper is a study on media-storm and news coverage blow-ups, analyzing them in depth, while also proposing a new article similarity model to compute pairwise similarity and create clusters.", "excitement": 3, "reproducibility": 2, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "sd6I19M3HB", "reviewer": "Reviewer_6B36", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for all of your helpful comments, suggestions, and critiques. We are encouraged by your statements expressing that “the analysis of media storms is interesting”, that “the dataset the authors are proposing is solid”, and that our work is “well written and easy to follow”. We also note that our model and data may be used in not only the analysis of media storms, but rather in any context where scholars are interested in articles which are written about the same event, concept, or topic.\n \nRegarding your suggestions for additional analyses that we could have included, we love the number and variety of questions that you think would be interesting to study, and we hope that our work will enable subsequent investigations along these lines, both by formalizing and characterizing the underlying phenomenon, and by releasing our code and data. Unfortunately, given the available space, we had to make hard choices about what to include. While Boydstun et al.’s paper (2014) introducing media storms was published nearly a decade ago, questions surrounding the mechanisms of media storms and their existence across many outlets of different types have been left unanswered. Our study is the first to test these mechanisms empirically, and this felt like the most important first step towards advancing this research agenda. We hope that our detailed characterization of media storms, along with our analyses of gatekeeping and intermedia agenda setting, will help to further research in this area, including of the kinds of questions you suggest.\n\nFinally, although we were not able to address the downstream effects of media storms directly, our characterization of media storms is consequential because it is linked to downstream effects shown in prior research. Boydstun et al. (2014) provide four case studies showing that Google searches are responsive to media storms. Broader follow up work shows that news stories have greater influence on congressional attention when they take part in a media storm (Walgrave, 2017). In conclusion, our work provides evidence for unanswered questions regarding media storms, introduces resources for general communication research, and opens the door to continued work on the effect of media storms on public opinion and politics. \n\nBoydstun, A. E., Hardy, A., & Walgrave, S. (2014). Two Faces of Media Attention: Media Storm Versus Non-Storm Coverage. Political Communication, 31(4), 509–531. https://doi.org/10.1080/10584609.2013.875967\n\nWalgrave, S., Boydstun, A. E., Vliegenthart, R., & Hardy, A. (2017). The nonlinear effect of information on political attention: Media storms and US congressional hearings. Political Communication, 34(4), 548–570.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- This paper is an explicit study on media storms. I agree with the authors that the analysis of media storms is interesting and that the community can gain lots of insight from them.\n- While the authors didn’t use a large amount of data to train their article similarity model, they ran it on a large number of articles to create a large dataset. This dataset can be useful for future work to study media storms.\n- The analysis done by the authors confirms what readers expect should happen in media storms. However, the fact that this happens over the authors large analysis confirms that the dataset the authors are proposing is solid.\n- The paper is well written and easy to follow.", "reasons_to_reject": "- The main thing I would have liked to see in this paper is some results using the media storms data for a downstream task. Some of these could also be tasks the authors propose, like identifying trends in future media storms based on historical media storms.\n    - Instead, this paper mostly just focuses on analyzing the storms (and some of the results are not surprising given the definition of media storms, like the fact that topic coverage increases). It would be cool if the authors also use the storms data for other tasks, or at least discuss how it can be used. Here are some examples I can think of:\n        - Can we predict when a media storm is about to happen? When it’s going to end? How long is it going to be?\n        - Can we predict which users are likely to be involved in a media storm? Sources? How does their perspectives change over time?\n            - Can we predict which outlets are likely to lead the coverage? During a storm, can we determine this based on past storm data?\n        - Do some media storms cover more factual information and others spread more fake news? When does each tend to happen?\n            - This can be useful if we know a media storm is happening, then maybe we should analyze the data around the media storm more carefully.\n    - Apart from the above, the main contribution of this paper is a dataset and an analysis of it, which in my opinion doesn't make the contribution of the paper extremely strong, thus my ambivalent Excitement score.", "questions_to_authors": "", "paper_topic_and_main_contributions": "- This paper aims to analyze news media storms, periods of times in the news media cycle where outlets increase their coverage of an event and also more users interact with it. They propose a model based on article similarity to identify the media storms, identifying 98 of them over a period of 21 months. They then run a large amounts of analysis, confirming existing theories about media storms (such as the fact that they tend to focus more on certain topics or that outlets mimic each other during a media storm). \n- Specifically, the authors first uses large amounts from the NELA corpus (millions of articles) and a news similarity model to identify clusters that they say are the media storms. They train the article-similarity model using a dataset of ~5K pairs of articles labeled by humans for their similarity. The article-similarity model is a bi-encoder Neural Network model, trained to minimize the MSE between the cosine similarity of the articles embeddings created by the model and the human similarity ratings. They then use the article-similarity model and their definition of media storms to identify the storms. \n- Once identified, the authors analyze different aspects of the media storms:\n    - Duration: ~15 days\n    - How many outlets report on each storm (~76)\n    - How media storms develop: explosively, peaking immediately and sometimes having multiple peaks. \n    - The topic distributions: Focus on certain topics, in particular politics where war or entertainment is less focused on\n    - The way the storm develops: Either how different outlets copy each other (i.e. local outlets copy reliable outlets) or how coverage of topics increases during the storm (suggesting that storms disrupt normal publishing routines).", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper presents a method to detect and analyze news media storms, which are periods of intense and focused coverage of a certain event, issue, or topic. The paper uses a large corpus of news articles from various sources and a neural network model to measure the similarity between articles. The paper then clusters the articles based on their similarity, time, and named entities to identify the media storms. The paper also examines the characteristics and dynamics of the media storms, such as their topics, duration, diversity, and inter-media influence. Overall, the paper makes contribution towards proposing a scalable news similarity detection model and the availability of the resulting dataset can help subsequent future works on media storms.

I thank the reviewers for providing useful suggestions and the authors for providing relevant responses to the concerns raised. I encourage the authors to incorporate these discussions in the future versions of the paper, such as, providing the rationale for using particular portions of the documents for similarity computation or for the specific choice of parameters for determining media storm, apart from addressing the confusions surrounding Table 1 and Figure 5.
Decision: Accept-Findings