Megan Brown
🛑 👉🏻👉🏻👉🏻 INFORMATION AVAILABLE CLICK HERE👈🏻👈🏻👈🏻
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Megan Brown Martinez began working as an actor in 1998. She's had the good fortune to work alongside such notable names as Kevin Costner, William Hurt, Brad Pitt and Val Kilmer. Her most recent projects include Mister Mayfair with Armand Assante and My Dad's Christmas Date starring Jeremy Piven. She graduated from Columbia University in 2005 with ...
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Born:
February 25 ,
1976
in
St. Petersburg, Florida, USA
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Кто Вы, Мистер Брукс?
Dance Couple (Woman)
Смертельная битва. Завоевание
Mileena
Главнокомандующий
Jessica Thompson
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Actress
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Mister Mayfair
( completed )
Madam Latham
The Spyce of Life
( completed )
Madame Latham
2020
My Dad's Christmas Date
Claire (as Megan Brown Martinez)
2019
Главнокомандующий
Jessica Thompson
2009
Парниковый эксперимент
Catherine
2009
Пир 3: Счастливая кончина
(Video)
Woman Stranger
2008
Загадочная история Бенджамина Баттона
Woman Kissing Benjamin (uncredited)
2007
Кто Вы, Мистер Брукс?
Dance Couple (Woman)
2007
Mexican Sunrise
Willy Red (scenes deleted)
- Shadow of a Doubt
(1999)
... Mileena
One Year Off
(associate producer) ( post-production )
Other Works:
She played the lead role, Anna, opposite Emmy Award winner Pruitt Taylor Vince in a run of Lanford Wilson's "Burn This" in Shreveport, Louisiana.
Alternate Names:
Megan Brown Martinez
Spouse:
Philippe Martinez
Trivia:
Skilled in Kickboxing, painting, rollerblading, skiing and is an aerobics instructor.
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I am a research scientist/data engineer for the Center for Social Media and Politics @ NYU.
I received my BA in Politics from New York University in 2019.
Currently, I study misinformation, disinformation, and algorithms in the online media ecosystem. On the side, I dabble in photography .
My research is centered on the online information ecosystem. My work spans Twitter
disinformation campaigns by the Internet Research Agency, the YouTube recommendation algorithm,
and the online conversation on presidential debates.
With Zeve Sanderson, Richard Bonneau, Jonathan Nagler, and Joshua A. Tucker
We analyze the spread of Donald Trump’s tweets that were flagged by Twitter using two intervention strategies—attaching a warning label and blocking engagement with the tweet entirely. We find that while blocking engagement on certain tweets limited their diffusion, messages we examined with warning labels spread further on Twitter than those without labels. Additionally, the messages that had been blocked on Twitter remained popular on Facebook, Instagram, and Reddit, being posted more often and garnering more visibility than messages that had either been labeled by Twitter or received no intervention at all. Taken together, our results emphasize the importance of considering content moderation at the ecosystem level.
Full Article |
USA Today |
Tech Policy Press |
Popular Science |
CNET |
Replication Materials
With Fuqing Wu, Amy Xiao, Jianbo Zhang, Katya Moniz, Noriko Endo, Frederica Armas, Richard Bonneau, Mary Bushman, Peter R. Chai, Claire Duvallet, Timothy B. Erickson, Katelyn Foppe, Newsha Ghaeli, Xiaoqiong Gu, William P. Hanage, Katherine H. Huang, Wei Lin Lee, Mariana Matus, Kyle A. MacElroy, Jonathan Nagler, Steven T. Rhode, Mauricio Santillana, Joshua A. Tucker, Stefan Wuertz, Shijie Zhao, Janelle Thompson, and Eric J. Alm
Current estimates of COVID-19 prevalence are largely based on symptomatic, clinically diagnosed cases. The existence of a large number of undiagnosed infections hampers population-wide investigation of viral circulation. Here, we use longitudinal wastewater analysis to track SARS-CoV-2 dynamics in wastewater at a major urban wastewater treatment facility in Massachusetts, between early January and May 2020. SARS-CoV-2 was first detected in wastewater on March 3. Viral titers in wastewater increased exponentially from mid-March to mid-April, after which they began to decline. Viral titers in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 4-10 days earlier in wastewater than in clinical data. We inferred viral shedding dynamics by modeling wastewater viral titers as a convolution of back-dated new clinical cases with the viral shedding function of an individual. The inferred viral shedding function showed an early peak, likely before symptom onset and clinical diagnosis, consistent with emerging clinical and experimental evidence. Finally, we found that wastewater viral titers at the neighborhood level correlate better with demographic variables than with population size. This work suggests that longitudinal wastewater analysis can be used to identify trends in disease transmission in advance of clinical case reporting, and may shed light on infection characteristics that are difficult to capture in clinical investigations, such as early viral shedding dynamics.
With Yevgeniy Golovchenko, Cody Buntain, Gregory Eady, and Joshua A. Tucker
This paper investigates online propaganda strategies of the Internet Research Agency (IRA)—Russian “trolls”—during the 2016 U.S. presidential election. We assess claims that the IRA sought either to (1) support Donald Trump or (2) sow discord among the U.S. public by analyzing hyperlinks contained in 108,781 IRA tweets. Our results show that although IRA accounts promoted links to both sides of the ideological spectrum, “conservative” trolls were more active than “liberal” ones. The IRA also shared content across social media platforms, particularly YouTube—the second-most linked destination among IRA tweets. Although overall news content shared by trolls leaned moderate to conservative, we find troll accounts on both sides of the ideological spectrum, and these accounts maintain their political alignment. Links to YouTube videos were decidedly conservative, however. While mixed, this evidence is consistent with the IRA’s supporting the Republican campaign, but the IRA’s strategy was multifaceted, with an ideological division of labor among accounts. We contextualize these results as consistent with a pre-propaganda strategy. This work demonstrates the need to view political communication in the context of the broader media ecology, as governments exploit the interconnected information ecosystem to pursue covert propaganda strategies.
With Zhanna Terechshenko, Niklas Loynes, Tom Paskhalis, and Jonathan Nagler
We analyzed 11,286,346 tweets collected over the course of the first nine debates, which spanned across 11 nights from June 26, 2019 to February 19, 2020.
We found that civil rights and healthcare were particularly popular policy issues amongst tweeters. Conservatives were more likely to tweet about immigration,
and the economy, while liberals were more likely to tweet about civil rights, education, and the environment. Read the full report below.
Full Report |
The Washington Post
With Zeve Sanderson, Jonathan Nagler, Richard Bonneau, and Joshua Tucker | December 9, 2020
Methods Supplement |
Dataset
With Zeve Sanderson | October 22, 2020
With Zhanna Terechshenko, Niklas Loynes, Tom Paskhalis, and Jonathan Nagler | March 15, 2020
With James Bisbee, Angela Lai, Joshua A. Tucker, Richard Bonneau, and Jonathan Nagler
With James Bisbee, Angela Lai, Joshua A. Tucker, Richard Bonneau, and Jonathan Nagler
With James Bisbee, Joshua A. Tucker, Richard Bonneau, and Jonathan Nagler
With Zhanna Terechshenko, Rachel Connolly, Angela Lai, Tianxin Ji, Jonathan Nagler, Joshua A. Tucker, and Richard Bonneau
With Maggie Macdonald, Joshua A. Tucker, Richard Bonneau, and Jonathan Nagler
Check out (or contribute to!) open source projects for collecting, analyzing, and modelling information
about the online environment.
This dataset contains the public interest exception labels for tweets by various politicians and political organizations during the 2020 election period. Tweets were labelled
for whether they contained a "soft intervention," a "hard intervention," or "no intervention." For tweets that received an intervention, we report the intervention type, text, and URL.
GitHub |
Analysis |
Methods Supplement
As the largest social media platform amongst American adults, YouTube is vital to understanding the online media ecosystem.
This software package makes accessing YouTube data easier and faster with just a few lines of code.
PyPI |
GitHub |
Jupyter Notebook
By Vishakh Padmakumar and Zhanna Terechshenko
Smaberta is a python wrapper for interacting with huggingface transformer models. Smaberta
makes it easier to train, evaluate, predict, and finetune cutting-edge language models based
on transformers.
urlExpander is inteded to be used by social media researchers who want to do analysis of links.
Aside from collecting in-depth user engagement data, these services obfuscate the destination of the shortened URLs.
urlExpander was created to address this challenge in a scalable and robust manner. It does so by providing
utility functions to convert Tweets into link datasets, filter for known for link-shortening services (like bit.ly),
resolve shortened links, and parse the title and meta description from webpages. urlExpander and offers multithreaded
url expansion. The multithreaded url expansion was created to overcome the bottleneck of mass link expansion
through parallelization, minimizating http requests, caching results, and chunking the input into smaller pieces.
With Zhanna Terechshenko and Vishakh Padmakumar | December 8, 2020
https://www.imdb.com/name/nm1814057/
http://www.meganbrown.org/
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