News Borrowing Revisited: A 50-Year Perspective

2018 ◽  
Vol 95 (4) ◽  
pp. 909-929 ◽  
Author(s):  
Daniel Riffe ◽  
Seoyeon Kim ◽  
Meghan R. Sobel
Keyword(s):  
New York ◽  
Social Media ◽  
News Coverage ◽  
New York Times ◽  
Press Freedom ◽  
International News ◽  
Quarter Century ◽  
Social Media Data ◽  
Local Media ◽  
Media Data

Analyzing 50 years’ of New York Times international news coverage ( N = 20,765), this study extends research on the “shrinking international news hole,” levels of press freedom, agent (e.g., Times correspondent), and “borrowed” news—information gleaned from local media, including social media. Data show a recent, growing role for social media and an increase in news borrowing, while foreign coverage declined; slight resurgence in foreign coverage during the last quarter-century; reduced wire copy use but increased correspondent news borrowing; and increased coverage of but decreased news borrowing in news from non-free nations. Borrowing from social media was greatest in non-free nations.

scholarly journals Real-time spatio-temporal event detection on geotagged social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yasmeen George ◽  
Shanika Karunasekera ◽  
Aaron Harwood ◽  
Kwan Hui Lim
Keyword(s):  
New York ◽  
Social Media ◽  
Event Detection ◽  
Detection System ◽  
Time And Space ◽  
Social Media Data ◽  
Event Time ◽  
Spatio Temporal ◽  
Geographical Space ◽  
Media Data

AbstractA key challenge in mining social media data streams is to identify events which are actively discussed by a group of people in a specific local or global area. Such events are useful for early warning for accident, protest, election or breaking news. However, neither the list of events nor the resolution of both event time and space is fixed or known beforehand. In this work, we propose an online spatio-temporal event detection system using social media that is able to detect events at different time and space resolutions. First, to address the challenge related to the unknown spatial resolution of events, a quad-tree method is exploited in order to split the geographical space into multiscale regions based on the density of social media data. Then, a statistical unsupervised approach is performed that involves Poisson distribution and a smoothing method for highlighting regions with unexpected density of social posts. Further, event duration is precisely estimated by merging events happening in the same region at consecutive time intervals. A post processing stage is introduced to filter out events that are spam, fake or wrong. Finally, we incorporate simple semantics by using social media entities to assess the integrity, and accuracy of detected events. The proposed method is evaluated using different social media datasets: Twitter and Flickr for different cities: Melbourne, London, Paris and New York. To verify the effectiveness of the proposed method, we compare our results with two baseline algorithms based on fixed split of geographical space and clustering method. For performance evaluation, we manually compute recall and precision. We also propose a new quality measure named strength index, which automatically measures how accurate the reported event is.

scholarly journals Fashion informatics of the Big 4 Fashion Weeks using topic modeling and sentiment analysis

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yeong-Hyeon Choi ◽  
Seungjoo Yoon ◽  
Bin Xuan ◽  
Sang-Yong Tom Lee ◽  
Kyu-Hye Lee
Keyword(s):  
New York ◽  
Social Media ◽  
Network Analysis ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Semantic Network ◽  
Semantic Network Analysis ◽  
Social Media Data ◽  
Big 4 ◽  
Media Data

AbstractThis study used several informatics techniques to analyze consumer-driven social media data from four cities (Paris, Milan, New York, and London) during the 2019 Fall/Winter (F/W) Fashion Week. Analyzing keywords using a semantic network analysis method revealed the main characteristics of the collections, celebrities, influencers, fashion items, fashion brands, and designers connected with the four fashion weeks. Using topic modeling and a sentiment analysis, this study confirmed that brands that embodied similar themes in terms of topics and had positive sentimental reactions were also most frequently mentioned by the consumers. A semantic network analysis of the tweets showed that social media, influencers, fashion brands, designers, and words related to sustainability and ethics were mentioned in all four cities. In our topic modeling, the classification of the keywords into three topics based on the brand collection’s themes provided the most accurate model. To identify the sentimental evaluation of brands participating in the 2019 F/W Fashion Week, we analyzed the consumers’ sentiments through positive, neutral, and negative reactions. This quantitative analysis of consumer-generated social media data through this study provides insight into useful information enabling fashion brands to improve their marketing strategies.

Identifying urban crowds using geo-located Social media data: a Twitter experiment in New York City

2016 ◽  
Vol 48 (2) ◽  
pp. 287-308 ◽  
Author(s):  
Mohamed ben Khalifa ◽  
Rebeca P. Díaz Redondo ◽  
Ana Fernández Vilas ◽  
Sandra Servia Rodríguez
Keyword(s):  
New York ◽  
Social Media ◽  
New York City ◽  
York City ◽  
Social Media Data ◽  
Media Data

scholarly journals “Sometimes da #beachlife ain't always da wave”: Understanding People’s Evolving Hurricane Risk Communication, Risk Assessments, and Responses Using Twitter Narratives

2018 ◽  
Vol 10 (3) ◽  
pp. 537-560 ◽  
Author(s):  
Julie L. Demuth ◽  
Rebecca E. Morss ◽  
Leysia Palen ◽  
Kenneth M. Anderson ◽  
Jennings Anderson ◽  
...  
Keyword(s):  
New York ◽  
Social Media ◽  
Risk Perceptions ◽  
Significant Risk ◽  
Hurricane Sandy ◽  
Risk Assessments ◽  
Environmental Cues ◽  
Social Media Data ◽  
Coping Responses ◽  
Media Data

Abstract This article investigates the dynamic ways that people communicate, assess, and respond as a weather threat evolves. It uses social media data, which offer unique records of what people convey about their real-world risk contexts. Twitter narratives from 53 people who were in a mandatory evacuation zone in a New York City neighborhood during Hurricane Sandy in 2012 were qualitatively analyzed. The study provides rich insight into the complex, dynamic information behaviors and risk assessments of people at risk, and it illustrates how social media data can be collected, sampled, and analyzed to help provide this understanding. Results show that this sample of people at significant risk attended to forecast information and evacuation orders as well as multiple types of social and environmental cues. Although many tweeted explicitly about the mandatory evacuation order, forecast information was usually referenced only implicitly. Social and environmental cues grew more important as the threat approached and often triggered heightened risk perceptions or protective actions. The results also reveal the importance of different aspects of people’s cognitive and affective risk perceptions as well as specific emotions (e.g., fear, anger) for understanding risk assessments. People discussed a variety of preparatory and protective behavioral responses and exhibited multiple types of coping responses (e.g., humor) as the threat evolved. People’s risk assessments and responses were closely intertwined, and their risk perceptions were not continuously elevated as the hurricane approached; they exhibited different ways of interpreting, coping, and responding as they accessed and processed evolving information about the threat.

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