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Influence et contagion
L'influence et la contagion dans la cyberculture
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The #Neuroscience of Social #Influence | Beautiful Minds

The #Neuroscience of Social #Influence | Beautiful Minds | Influence et contagion | Scoop.it
Before I wrote this article, I went through two stages. In the first stage, I cruised the academic journals for interesting papers. Once I found a ...
luiy's insight:

Can the pattern of neurons firing in my brain predict how much this article will be retweeted on twitter?

 

A recent study conducted by Emily Falk, Matthew Lieberman, and colleagues gets us closer to answering these important questions. The researchers recruited undergraduate participants and randomly assigned them to two groups: the “interns” and the “producers.” The 20 interns were asked to view ideas for television pilots and provide recommendations to the 79 producers about which shows should be considered for further development and production. All of the interns had their brains scanned by fMRI while they viewed the videos, and they were then videotaped while they discussed the merits of each pilot show idea. The producers rated which ideas they would like to further recommend. How was neural activity related to the spread of ideas?

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Ebola Twitter Network | #SNA #influence #gephi

Ebola Twitter Network | #SNA #influence #gephi | Influence et contagion | Scoop.it
Introduction During the week beginning 15 September I collected 240k Tweets containing the word ‘ebola’. The following information was extracted from the Twitter Search API: Tweet: Text, Created At, Favorites User: Name, Followers, Following, Location ReTweets Mentions Tags Reply To...
luiy's insight:

TOOLS

 

More to follow in next post but here is a brief list of the tools used to create this post. 

 

- Data Collection: Python 

 

- Graph Selection: Neo4j graph database to query nodes and create the network. 

 

- Data Analysis: R 

 

- Visualisation: Gephi

 

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Nicholas Christakis: The #Sociological Science Behind Social #Networks and Social #Influence | #SNA

If You're So Free, Why Do You Follow Others? The Sociological Science Behind Social Networks and Social Influence. Nicholas Christakis, Professor of Medical ...

luiy's insight:

If you think you're in complete control of your destiny or even your own actions, you're wrong. Every choice you make, every behavior you exhibit, and even every desire you have finds its roots in the social universe. Nicholas Christakis explains why individual actions are inextricably linked to sociological pressures; whether you're absorbing altruism performed by someone you'll never meet or deciding to jump off the Golden Gate Bridge, collective phenomena affect every aspect of your life. By the end of the lecture Christakis has revealed a startling new way

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Bill Aukett's curator insight, September 29, 8:34 PM

Human networks as complex systems?

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Who is central to a social network? It depends on your centrality #measure | #sna #influence #basics

Who is central to a social network? It depends on your centrality #measure | #sna #influence #basics | Influence et contagion | Scoop.it
luiy's insight:

One important feature of networks is the relative centrality of individuals in them.  Centrality is a structural characteristic of individuals in the network, meaning a centrality score tells you something about how that individual fits within the network overall.  Individuals with high centrality scores are often more likely to be leaders, key conduits of information, and be more likely to be early adopters of anything that spreads in a network. 

 

- Individuals who are highly connected to others within their own cluster will have a high closeness centrality.

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The Harlem Shake Story: Birth of a #Meme | #SNA #virality #datascience

The Harlem Shake Story: Birth of a #Meme | #SNA #virality #datascience | Influence et contagion | Scoop.it
A series of remixed videos along with a number of key communities around the world triggered a rapid escalation, giving the meme widespread global visibility. Who were the initial communities behind this mega-trend? SocialFlow took a look at 1.9 million tweets during a two-week period that included the words ’harlem shake’, or some versions of it. 
luiy's insight:

Social Flow looked at the social connections amongst users who were posting to the meme. This gave them the ability to identify the underlying communities engaging with the meme at a very early stage. In the graph above each node represents a user that was actively posting and referencing the Harlem Shake meme on Feb 7 or 8 to Twitter. Connections between users reflect follow/friendship relationships. The graph is organized using a force directed algorithm, and colored based on modularity, highlighting dominant clusters - regions in the graph which are much more interconnected. These clusters represent groups of users who tend to have some attribute in common. The purple region in the graph (left side) represents African American Twitter users who are referencing Harlem Shake in its original context. There's very little density there as it is not really a tight-knit community, but rather a segment of users who are culturally aligned, and are clearly much more interconnected amongst themselves than with other groups.

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Handbook of Network Analysis | #SNA #taxonomy

Handbook of Network Analysis | #SNA #taxonomy | Influence et contagion | Scoop.it

Taxonomy of Networks

luiy's insight:

This is the Handbook of Network Analysis, the companion article to the KONECT (Koblenz Network Collection) project. This project is intended to collect network datasets, analyse them systematically, and provide both datasets and the underlying network analysis code to researchers. This article outlines the project, gives all definitions used within the project, reviews all network statistics used, reviews all network plots used, and gives a brief overview of the API used by KONECT.

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Knowledge Sharing #Tools and #Methods Toolkit - #SNA

Knowledge Sharing #Tools and #Methods Toolkit - #SNA | Influence et contagion | Scoop.it

"Social network analysis is the mapping and measuring of relationships and flows between people, groups, organisations, computers or other information/knowledge processing entities." (Valdis Krebs, 2002). Social Network Analysis (SNA) is a method for visualizing our people and connection power, leading us to identify how we can best interact to share knowledge.


Via jean lievens, Nevermore Sithole, João Greno Brogueira
luiy's insight:

When to use:Visualize relationships within and outside of the organization.Facilitate identification of who knows who and who might know what - teams and individuals playing central roles - thought leaders, key knowledge brokers, experts, etc.Identify isolated teams or individuals and knowledge bottlenecks.Strategically work to improve knowledge flows.Accelerate the flow of knowledge and information across functional and organisational boundaries.Improve the effectiveness of formal and informal communication channels.Raise awareness of the importance of informal networks.

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Karen du Toit's curator insight, September 15, 3:27 AM

A great wiki to check out about social network analysis

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Truthy: Information Diffusion in Online Social Networks | #influence #virality #SNA

Truthy: Information Diffusion in Online Social Networks | #influence #virality #SNA | Influence et contagion | Scoop.it
luiy's insight:

The focus of this research project is understanding how information propagates through complex socio-technical information networks. Leveraging large-scale public data from online social networking platforms, we are able to analyze and model the spread of information, from political discourse to market trends, from news to social movements, and from trending topics to scientific results, in unprecedented detail.

 

We study how popular sentiment, user influence, attention, social network structure, and other factors affect the manner in which information is disseminated. Additionally, an important goal of the Truthy project is to better understand how social media can be abused, for example by astroturfing.

 

Our work to date includes a number of core research themes:

 

1. We study how individuals’ limited attention span affects what information we propagate and what social connections we make, and how the structure of social networks can help predict which memes are likely to become viral.

 

2. We explore social science questions via social media data analytics. Examples of research to date include analyses of geographic and temporal patterns in movements like Occupy Wall Street, societal unrest in Turkey, polarization and cross-ideological communication in online political discourse, partisan asymmetries in online political engagement, the use of social media data to predict election outcomes and forecast key market indicators, and the geographic diffusion of trending topics.

 

3. Truthy is an ensemble of web services and tools to demonstrate applications of our data mining research, from visualizing meme diffusion patterns to detecting social bots on Twitter.

 

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BeerBergman's curator insight, September 3, 5:45 PM

"luiy's insight:

The focus of this research project is understanding how information propagates through complex socio-technical information networks. Leveraging large-scale public data from online social networking platforms, we are able to analyze and model the spread of information, from political discourse to market trends, from news to social movements, and from trending topics to scientific results, in unprecedented detail.

 

We study how popular sentiment, user influence, attention, social network structure, and other factors affect the manner in which information is disseminated. Additionally, an important goal of the Truthy project is to better understand how social media can be abused, for example by astroturfing."

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A Look Inside Those 1.1 Million Open-Internet Comments | #datascience #complexity #SNA

A Look Inside Those 1.1 Million Open-Internet Comments | #datascience #complexity #SNA | Influence et contagion | Scoop.it
These cluster maps give us a two-dimensional look at the complex arguments Americans posted on the topic of net neutrality. One theme in the comments had to do with the American dream.
luiy's insight:

How To Read This Cluster Map

 

- Similar nodes typically cluster together and clusters are grouped by color

- Each node represents a news story; a node sized by degree represents number of connections (i.e., similarity) to other nodes

- Connections represent similar language used across nodes

- A node bridging two clusters can indicate a story that synthesizes multiple topics

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US #military studied how to #influence Twitter users in #Darpa-funded research

US #military studied how to #influence Twitter users in #Darpa-funded research | Influence et contagion | Scoop.it
Defense Department spent millions researching users, including studies on Occupy and Middle East residents, and how to better spread propaganda

Via Pierre Levy
luiy's insight:

The activities of users of Twitter and other social media services were recorded and analysed as part of a major project funded by the US military, in a program that covers ground similar to Facebook’s controversial experiment into how to control emotions by manipulating news feeds.

 

Research funded directly or indirectly by the US Department of Defense’s military research department, known as Darpa, has involved users of some of the internet’s largest destinations, including Facebook, Twitter, Pinterest and Kickstarter, for studies of social connections and how messages spread.

 
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Mapping the Information #Economy: A Tale of Five Industries | #patterns #SNA

Mapping the Information #Economy: A Tale of Five Industries | #patterns #SNA | Influence et contagion | Scoop.it

At Box, we constantly measure customers’ engagement with our product to understand how to enhance user experience and help businesses be more productive and collaborative. With 25 million users at 225,000 businesses interacting with content 2.5 billion times quarterly, we have a unique vantage point on how enterprises in nearly every sector leverage the cloud.

luiy's insight:

But what, if anything, can the patterns in how businesses share information tell us about how they operate more generally? Zooming out, what might these patterns signal about entire industries and their relative preparedness for adapting to an increasingly information-driven economy? We’re entering an era where a company’s competitiveness is determined by its return on information – how democratized its access is, how fast it moves, and how quickly it can be updated and leveraged to generate value.

 

For our first Information Economy Report, we started by visually mapping the flow of information within customer organizations. Every red node represents an employee, every blue node an external collaborator, and every line a transfer of content, with thicker lines indicating more frequent sharing. The results were beautiful, and also telling.

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#Influence Explorer : explore how foreign entities influence #policy and public #opinion in the U.S. | #ddj

#Influence Explorer : explore how foreign entities influence #policy and public #opinion in the U.S. | #ddj | Influence et contagion | Scoop.it
Influence Explorer connects the dots of political contributions on the federal and state level allowing you to track influence by lawmaker, company or prominent individual.
luiy's insight:

Foreign Influence Explorer

 

After months of research, technical development and manual data entry, we are proud to unveil Foreign Influence Explorer—a new database housed within Influence Explorer that lets users explore how foreign entities influence policy and public opinion in the U.S.

The data comes from the Department of Justice and is collected according to the Foreign Agents Registration Act, which places stringent reporting requirements on foreign governments, political parties, businesses and other organizations that aim to influence policy here in the States.

 

The new database also includes a feed of proposed arms sales documents from the Defense Security Cooperation Agency. This data is included because so much foreign lobbying revolves around arms sales, which creates a nexus of influence between countries that want to buy U.S. arms and U.S. manufacturers that want to sell them.

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#Clustering #memes in social media streams | #algorithms #sna

#Clustering #memes in social media streams | #algorithms #sna | Influence et contagion | Scoop.it
luiy's insight:

The problem of clustering content in social media has pervasive applications, including the identification of discussion topics, event detection, and content recommendation. Here we describe a streaming framework for online detection and clustering of memes in social media, specifically Twitter.

 

A pre-clustering procedure, namely protomeme detection, first isolates atomic tokens of information carried by the tweets. Protomemes are thereafter aggregated, based on multiple similarity measures, to obtain memes as cohesive groups of tweets reflecting actual concepts or topics of discussion.

 

The clustering algorithm takes into account various dimensions of the data and metadata, including natural language, the social network, and the patterns of information diffusion. As a result, our system can build clusters of semantically, structurally, and topically related tweets.


The clustering process is based on a variant of Online K-means that incorporates a memory mechanism, used to "forget" old memes and replace them over time with the new ones. The evaluation of our framework is carried out by using a dataset of Twitter trending topics.


Over a one-week period, we systematically determined whether our algorithm was able to recover the trending hashtags. We show that the proposed method outperforms baseline algorithms that only use content features, as well as a state-of-the-art event detection method that assumes full knowledge of the underlying follower network. We finally show that our online learning framework is flexible, due to its independence of the adopted clustering algorithm, and best suited to work in a streaming scenario.

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Multilayer Networks tutorial | #SNA #models

These are the slides for a tutorial talk about "multilayer networks" that I gave at NetSci 2014. I walk people through a review article that I wrote with my …
luiy's insight:

Classifying Multilayer Networks

 

•  Special cases of multilayer networks include: mulplex networks, interdependent networks, networks of networks, node-­‐colored networks, edge-­‐colored mulgraphs, …

 

• To obtain one of these special cases, we impose constraints on the general structure defined earlier.

 

---------

 

Other Types of  Multilayer Networks

 

•  k-­‐partite graphs

– Bipartite networks are most commonly studied

 

• Coupled-­‐cell networks

– Associate a dynamical system with each node of a multigraph. Network structure through coupling terms.

 

• Multilevel networks – Very popular in social statistics literature (upcoming special issue of Social Networks)

– Each level is a layer

– Think ‘hierarchical’ situations. Example: ‘micro-­‐ level’ social network of researchers and a ‘macro-­‐ level’ for a research-­‐exchange network between laboratories to which the researchers belong.

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How Videos Go #Viral part | / #metrics #SNA #contagion

How Videos Go #Viral part | / #metrics #SNA #contagion | Influence et contagion | Scoop.it
luiy's insight:

This is a big post with a lot of variables and data. So let’s recap on what we’re saying overall. How do viral videos spread socially?

We can see there are 2 broad patterns of content diffusion. One model we call “spike” – the sudden ‘explosion’ of sharing activity – and the other we call “growth”, where popularity is a slower and steadier grower.  The metrics we’ve discussed, such as velocity, variability and social currency, provide a way to identify which kind of virality you’re looking:

 

 

http://www.facegroup.com/blog/how-videos-go-viral.html

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#Culturegraphy: the Cultural Influences and References between Movies | #DH #influence #dataviz

#Culturegraphy: the Cultural Influences and References between Movies | #DH #influence #dataviz | Influence et contagion | Scoop.it
luiy's insight:

Culturegraphy [culturegraphy.com], developed by "Information Model Maker" Kim Albrecht reveals represent complex relationships of over 100 years of movie references.

 

Movies are shown as unique nodes, while their influences are depicted as directed edges. The color gradients from blue to red that originate in the1980s denote the era of postmodern cinema, the era in which movies tend to adapt and combine references from other movies.

 

Although the visualizations look rather minimalistic at first sight, their interactive features are quite sophisticated and the resulting insights are naturally interesting. Therefore, do not miss out the explanatory movie below.

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Can #Ebola Be Stopped By Treating It Like A Terrorist Network? | #algorithms #health

Can #Ebola Be Stopped By Treating It Like A Terrorist Network? | #algorithms #health | Influence et contagion | Scoop.it
A Florida defense contractor is using the data mining tools of counterterrorism to take aim at Ebola.
luiy's insight:

Six months after its latest resurgence, the Ebola virus shows no signs of letting up. "We desperately need new strategies adapted to this reality," said Dr. Joanne Liu, international president ofDoctors Without Borders in a grim statement last week. One hope is that data, which can spread faster than disease, could give humans a technological leg up on the spread of the epidemic. The problem with this data is that it's massive and often unstructured.

 

Can scientists and medical professionals make sense of the mess in time for it to make a difference?

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Strongly Connected Component | #SNA #datascience

Strongly Connected Component | #SNA #datascience | Influence et contagion | Scoop.it
luiy's insight:

Graph connectivity is of special interest in networking, search, shortest path and many other applications.

 

Strongly connected directed graph has a path from all vertices to all vertices.

 

Strongly connected components (SCC) are the strongly connected subgraphs.

 

 - abe, fg, cd and h are the strongly connected subgraphs of G.

 

 

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graph-tool: Efficent network analysis with #python | #SNA #tools

graph-tool: Efficent network analysis with #python | #SNA #tools | Influence et contagion | Scoop.it
graph-tool: Efficent network analysis with python
luiy's insight:

An extensive array of features is included, such as support for arbitrary vertex, edge or graph properties, efficient "on the fly" filtering of vertices and edges, powerful graph I/O using the GraphML, GML and dot file formats, graph pickling, graph statistics (degree/property histogram, vertex correlations, average shortest distance, etc.), centrality measures, standard topological algorithms (isomorphism, minimum spanning tree, connected components, dominator tree, maximum flow, etc.), generation of random graphs with arbitrary degrees and correlations, detection of modules and communities via statistical inference ,,,,,, 

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#Google matrix analysis of directed networks | #datascience #algorithms

#Google matrix analysis of directed networks | #datascience #algorithms | Influence et contagion | Scoop.it
luiy's insight:

This review describes matrix tools and algorithms which facilitate classification and information retrieval from large networks recently created by human activity. The Google matrix formed by links of the network has typically a huge size. Thus, the analysis of its spectral properties including complex eigenvalues and eigenvec- tors represents a challenge for analytical and numerical methods. It is rather surprising, but the class of such matrices, belonging to the class of Markov chains and Perron-Frobenius operators, was practically not inves- tigated in physics. Indeed, usually the physical prob- lems belong to the class of Hermitian or unitary ma- trices. Their properties had been actively studied in the frame of Random Matrix Theory (RMT) (Akemann et al., 2011; Guhr et al., 1998; Mehta, 2004) and quantum chaos (Haake, 2010). The analytical and numerical tools developed in these research fields allowed to understand many universal and peculiar features of such matrices in the limit of large matrix size corresponding to many-body quantum systems (Guhr et al., 1998), quantum comput- ers (Shepelyansky , 2001) and a semiclassical limit of large quantum numbers in the regime of quantum chaos (Haake, 2010). In contrast to the Hermitian problem, the Google matrices of directed networks have complex eigenvalues. 

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Top 10 #algorithms in data mining | #datascience

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This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.

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#freeBook: Social Media Mining | #datascience #SNA #influence

#freeBook: Social Media Mining | #datascience #SNA #influence | Influence et contagion | Scoop.it
luiy's insight:

The Social Media Mining book is published by Cambridge University Press in 2014. Please see Cambridge’s page for the book for more information or if you are interested in obtaining an examination copy.

 

Download a complete pre-publicaiton draft of the Social Media Mining book in PDF format. The reader is allowed to take one copy for personal use but not for further distribution (either print or electronically). The book is available for purchase from Cambridge University Press and other distribution channels.

 

You can also download each chapter below:

 

• Chapter 1. Introduction to social media mining

 

Part I: Essentials
• Chapter 2. Graph essentials
• Chapter 3. Network measures
• Chapter 4. Network models
• Chapter 5. Data mining essentials

 

Part II: Communities and Interactions
• Chapter 6. Community analysis
• Chapter 7. Information diffusion in Social Media

 

Part III: Applications
• Chapter 8. Influence and homophily
• Chapter 9. Recommendation in social media
• Chapter 10. Behavior analytics

 

Download the Bibliography

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Evolution of Online User Behavior During a Social Upheaval | #datascience #diregeziparki

Evolution of Online User Behavior During a Social Upheaval | #datascience #diregeziparki | Influence et contagion | Scoop.it
luiy's insight:

Social media represent powerful tools of mass communication and information diffusion. They played a pivotal role during recent social uprisings and political mobilizations across the world. Here we present a study of the Gezi Park movement in Turkey through the lens of Twitter. We analyze over 2.3 million tweets produced during the 25 days of protest occurred between May and June 2013. We first characterize the spatio-temporal nature of the conversation about the Gezi Park demonstrations, showing that similarity in trends of discussion mirrors geographic cues. We then describe the characteristics of the users involved in this conversation and what roles they played. We study how roles and individual influence evolved during the period of the upheaval. This analysis reveals that the conversation becomes more democratic as events unfold, with a redistribution of influence over time in the user population. We conclude by observing how the online and offline worlds are tightly intertwined, showing that exogenous events, such as political speeches or police actions, affect social media conversations and trigger changes in individual behavior.

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Detecting #Emotional #Contagion in Massive Social #Networks

Detecting #Emotional #Contagion in Massive Social #Networks | Influence et contagion | Scoop.it
PLOS ONE: an inclusive, peer-reviewed, open-access resource from the PUBLIC LIBRARY OF SCIENCE. Reports of well-performed scientific studies from all disciplines freely available to the whole world.
luiy's insight:

Happiness and other emotions have recently been an important focus of attention in a wide range of disciplines, including psychology, economics, and neuroscience [1], [2], [3], [4]. Some of this work suggests that emotional states can be transferred directly from one individual to another via mimicry and the copying of emotionally-relevant bodily actions like facial expressions [5]. Experiments have demonstrated that people can “catch” emotional states they observe in others over time frames ranging from seconds to months [6], [7], and the possibility of emotional contagion between strangers, even those in ephemeral contact, has been documented by the effects of “service with a smile” on customer satisfaction and tipping [8].

 

Longitudinal data from face-to-face social networks has established that emotions as diverse as happiness [9], loneliness [10], and depression [11] are correlated between socially-connected individuals, and related work suggests that these correlations also exist online [4], [12], [13], [14], [15]. However, it is difficult to ascertain whether correlations in observational studies result from influencing the emotions of social contacts (contagion) or from choosing social contacts with similar emotions (homophily) [16].

 

Here, we propose an alternative method for detecting emotional contagion in massive social networks that is based on instrumental variables regression, a technique pioneered in economics [23]. In an experiment we would directly control each user's emotional expression to see what impact it has on their friends' emotional expression. However, since this is infeasible in our massive-scale setting, we identify a source of variation that directly affects the users' emotional expression (this variable is called an “instrument”). For this instrument, we use rainfall. Importantly, rainfall is unlikely to be causally affected by human emotional states, so if we find a relationship it suggests that rainfall influences emotional expression and not vice versa. We then measure whether or not the changes induced by the instrument predict changes in the friends' emotional expression. Instead of changing the user's emotion directly with an experimental treatment, we let rainfall do the work for us by measuring how much the rain-induced change in a user's expression predicts changes in the user's friends' expression.

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