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Influence et contagion
L'influence et la contagion dans la cyberculture
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5 Questions for Duncan Watts | #SNA #influence

luiy's insight:

Duncan J. Watts, principal researcher at Microsoft Research, is the 2014 winner of the Everett M. Rogers Award. The USC Annenberg Norman Lear Center got to sit down with him and ask him 5 questions about his talk "Social Influence in Markets & Networks: What's So Viral About Going "Viral"? 

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How #Network Science Is Changing Our Understanding of #Law | #SNA #patterns

How #Network Science Is Changing Our Understanding of #Law | #SNA #patterns | Influence et contagion | Scoop.it
The first network analysis of the entire body of European Community legislation reveals the pattern of links between laws and their resilience to change.

Via Complexity Digest
luiy's insight:

One of the more fascinating areas of science that has emerged in recent years is the study of networks and their application to everyday life. It turns out that many important properties of our world are governed by networks with very specific properties.

 

These networks are not random by any means. Instead, they are often connected in the now famous small world pattern in which any part of the network can be reached in a relatively small number of steps. These kinds of networks lie behind many natural phenomena such as earthquakes, epidemics and forest fires and are equally ubiquitous in social phenomena such as the spread of fashions, languages, and even wars.

 

So it should come as no surprise that the same kind of network should exist in the legal world. Today, Marios Koniaris and pals at the National Technical University of Athens in Greece show that the network of links between laws follows exactly the same pattern. They say their network approach provides a unique insight into the nature of the law, the way it has emerged and how changes may influence it in the future.

 

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Links that speak: The global #language network and its association with global fame | #SNA #DH

Links that speak: The global #language network and its association with global fame | #SNA #DH | Influence et contagion | Scoop.it
luiy's insight:
Languages vary enormously in global importance because ofhistorical, demographic, political, and technological forces. How-ever, beyond simple measures of population and economic power,there has been no rigorous quantitative way to define the globalinfluence of languages. Here we use the structure of the networksconnecting multilingual speakers and translated texts, as expressedin book translations, multiple language editions of Wikipedia, andTwitter, to provide a concept of language importance that goesbeyond simple economic or demographic measures. We find thatthe structure of these three global language networks (GLNs)is centered on English as a global hub and around a handfulof intermediate hub languages, which include Spanish, German,French, Russian, Portuguese, and Chinese. We validate the mea-sure of a language’s centrality in the three GLNs by showing that itexhibits a strong correlation with two independent measures ofthe number of famous people born in the countries associatedwith that language. These results suggest that the position ofa language in the GLN contributes to the visibility of its speakersand the global popularity of the cultural content they produce.
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GALLERY SocioPatterns project. SocioPatterns.org | #SNA #datascience

GALLERY SocioPatterns project. SocioPatterns.org | #SNA #datascience | Influence et contagion | Scoop.it
A gallery that offers a collection of visualizations, pictures, movies and other media created and/or recorded in the context of the SocioPatterns project.
luiy's insight:

Dynamical Contact Patterns in a Primary School

 

This movie represents the dynamical contacts network measured during one day of activity in a primary school. Nodes represent individuals, and edges indicate face-to-face contacts. Every frame shows the contact network over a time window of 20 minutes. Nodes are arranged in groups that correspond to the school classes, with the teacher node at the center. Nodes are color-coded according to the grade  and teachers are shown in black. This movie is included in the supplementary information of our PLoS ONE paper. The network visualization was created by Alain Barrat and André Panisson using Gephi. The cumulative social network of interaction is available from the corresponding dataset page.

 

- See more at: http://www.sociopatterns.org/gallery/#sthash.ae3X56fs.dpuf

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The Evolution of Beliefs over Signed Social Networks | #SNA #influence

The Evolution of Beliefs over Signed Social Networks | #SNA #influence | Influence et contagion | Scoop.it
luiy's insight:

We study the evolution of opinions (or beliefs) over a social network modeled as a signed graph. The sign attached to an edge in this graph characterizes whether the corresponding individuals or end nodes are friends (positive links) or enemies (negative links). Pairs of nodes are randomly selected to interact over time, and when two nodes interact, each of them updates its opinion based on the opinion of the other node and the sign of the corresponding link. This model generalizes DeGroot model to account for negative links: when two enemies interact, their opinions go in opposite directions. We provide conditions for convergence and divergence in expectation, in mean-square, and in almost sure sense, and exhibit phase transition phenomena for these notions of convergence depending on the parameters of the opinion update model and on the structure of the underlying graph. We establish a {\it no-survivor} theorem, stating that the difference in opinions of any two nodes diverges whenever opinions in the network diverge as a whole. We also prove a {\it live-or-die} lemma, indicating that almost surely, the opinions either converge to an agreement or diverge. Finally, we extend our analysis to cases where opinions have hard lower and upper limits. In these cases, we study when and how opinions may become asymptotically clustered to the belief boundaries, and highlight the crucial influence of (strong or weak) structural balance of the underlying network on this clustering phenomenon.

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Classic Authors Who Suck, According To Other Classic Authors | #controverses #dataviz #SNA

Classic Authors Who Suck, According To Other Classic Authors |  #controverses #dataviz #SNA | Influence et contagion | Scoop.it

Do you hate negative book reviews? Try telling that to Mark Twain, D.H. Lawrence, or any of these famous authors who have trashed their fellow writers' publications!

Photos: Associated Press, Getty
Jan Diehm for The Huffington Post

http://big.assets.huffingtonpost.com/20140514A_AuthorInsults.html

luiy's insight:

For every great author, there’s another great author eager to knock him or her down a few pegs. Although the writers on this map are typically deemed canonical by literary tastemakers, there wasn’t much mutual admiration amongst them.We’ve mapped out the rivalries and one-sided vendettas of many celebrated writers; just hover over an arrow between two authors to see a cutting insult directed by one to the other.

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Socilab - LinkedIn Social Network Visualization, Analysis, and Education | #SNA #influence

Socilab - LinkedIn Social Network Visualization, Analysis, and Education | #SNA #influence | Influence et contagion | Scoop.it
luiy's insight:

Socilab is a free tool that allows users to visualize, analyze, and download data on their LinkedIn network. It works with the LinkedIn API to a) calculate structural hole metrics such as network density, hierarchy and constraint - and displays your percentile compared to other users of the tool, b) display a dynamic/interactive visualization of your ego network with node coloring by industry and an option to enable/disable connections to self using D3.js, and c) produce a CSV adjacency matrix or Pajek edgelist for download and import into your favorite SNA package. Users might find it useful for class tutorials and/or quickly and cheaply fielding crude network surveys. Former users of the now deprecated LinkedIn inMaps may find this to be a useful alternative.

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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, 2014 8:34 PM

Human networks as complex systems?

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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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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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How Steve Jobs Connected It All: An Interactive Look at #Apple’s Technology #History | #influence #SNA

How Steve Jobs Connected It All: An Interactive Look at #Apple’s Technology #History | #influence #SNA | Influence et contagion | Scoop.it
luiy's insight:

Data sources & methodology

 

The networks in this post are based on all Apple patents published between January 1978 and October 2014. To avoid duplicate entries based on applications for the same invention in multiple countries, only representatives of so-called patent families (groups of patents disclosing the same invention in multiple countries) are included. This leads to a total dataset of 9,663 patents listing 5,272 unique inventors over the 36 years analysed. Inventor names were deduplicated using a pattern-matching algorithm – any false negatives and positives were taken care of manually. This was done to avoid e.g. ‘Steve Jobs’ and ‘Stephen P Jobs’ appearing as two separate inventors. Inventors (circles) are connected in the networks whenever they appear as co-inventors on a patent; patents (squares) are connected to inventors whenever an inventor appears on that patent. Colours were assigned to patents and inventors based on the network cluster they reside in – clusters were identified using an algorithm which groups nodes when they are densely connected internally, but sparsely connected to other groups.

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Structural #Patterns of the Occupy Movement on Facebook | #socialchange #SNA

Structural #Patterns of the Occupy Movement on Facebook | #socialchange #SNA | Influence et contagion | Scoop.it

In this work we study a peculiar example of social organization on Facebook: the Occupy Movement -- i.e., an international protest movement against social and economic inequality organized online at a city level. We consider 179 US Facebook public pages during the time period between September 2011 and February 2013. The dataset includes 618K active users and 753K posts that received about 5.2M likes and 1.1M comments. By labeling user according to their interaction patterns on pages -- e.g., a user is considered to be polarized if she has at least the 95% of her likes on a specific page -- we find that activities are not locally coordinated by geographically close pages, but are driven by pages linked to major US cities that act as hubs within the various groups. Such a pattern is verified even by extracting the backbone structure -- i.e., filtering statistically relevant weight heterogeneities -- for both the pages-reshares and the pages-common users networks.

 

Structural Patterns of the Occupy Movement on Facebook
Michela Del Vicario, Qian Zhang, Alessandro Bessi, Fabiana Zollo, Antonio Scala, Guido Caldarelli, Walter Quattrociocchi

http://arxiv.org/abs/1501.07203


Via Complexity Digest
luiy's insight:
Data Description The dataset represents a complete screenshot of the Occupy Movement in the period immediately following the outbreak of the protest on September 17th, 2011 in the Zuccotti Park of New York. The dataset covers all the posts until the end of February 2013, at the time when all the major protests were no more active. After the Zuccotti occupation, in fact, an October full of similar occupational events followed, leading to an international protest movement that extended itself until the end of 2012, when the movement was principally an online collective protest.
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Dynamical Contact #Patterns in a Primary School | #SNA #learning

luiy's insight:

This movie represents the dynamical evolution of the contacts during the first day of a deployment of the SocioPatterns sensing platform, see sociopatterns.org. Each dot represents an individual, and an edge is drawn when a contact between two individuals occurs. Only contacts lasting at least 40 s are retained. Each frame corresponds to an aggregation of the contact network over a time window of 20 mn, and successive frames correspond to aggregation time windows shifted by 10 s; the movie is then built using 20 frames per second. Nodes are disposed in circles corresponding to the various classes, with the teacher at the center, and color-coded according to the grade (teachers are shown in black).

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The Divination Network of Tarot | #SNA

The Divination Network of Tarot | #SNA | Influence et contagion | Scoop.it
luiy's insight:

Tarot is a deck of cards used since the 15th century to play various games as well as for divination purposes. We at Nodus Labs studied the structure of various Tarot decks, treating the cards as the nodes and relations between them as edges, building a graph of relations between the cards that are invariant across various Tarot decks. We discovered that the structure of the resulting graph has a very specific community structure, which makes Tarot a very efficient tool for telling narratives. We are currently working on practical implementations of this study.

 

This research was performed by Dmitry Paranyushkin and Colin Johnco.

 

http://noduslabs.com/cases/divination-network-tarot/

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Social Networks Visualizer | #SNA #Tools

Social Networks Visualizer | #SNA #Tools | Influence et contagion | Scoop.it
Social Networks Visualizer KDE-Apps.org Community Portal for KDE Applications Software Office Multimedia Graphic Network Games Utilities Screensaver QT Linux
luiy's insight:

It lets you construct networks (mathematical graphs) with a few clicks on a virtual canvas or load networks of various formats (GraphML, GraphViz, Adjacency, Pajek, UCINET, etc). Also, SocNetV enables you to modify the social networks, analyse their social and mathematical properties and apply visualization layouts for relevant presentation.

Furthermore, random networks (Erdos-Renyi, Watts-Strogatz, ring lattice, etc) and known social network datasets (i.e. Padgett's Florentine families) can be easily recreated. SocNetV also offers a built-in web crawler, allowing you to automatically create networks from links found in a given initial URL.

The application computes basic graph properties, such as density, diameter, geodesics and distances (geodesic lengths), connectedness, eccentricity, etc. It also calculates advanced structural measures for social network analysis such as centrality and prestige indices (i.e. closeness centrality, betweeness centrality, information centrality, power centrality, proximity and rank prestige), triad census, cliques, clustering coefficient, etc.

 

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World Cup 2014 - Germany vs. Argentina | #SNA #dataviz

World Cup 2014 - Germany vs. Argentina | #SNA #dataviz | Influence et contagion | Scoop.it
luiy's insight:

The 2014 World Cup in Brazil has begun. Check HuffPost's World Cup dashboard throughout the tournament for standings, schedules, and detailed summaries of each match.

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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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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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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, 2014 3:27 AM

A great wiki to check out about social network analysis

Chad Gorski's curator insight, May 18, 5:29 PM

Provides links to a variety of social network analysis tools

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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, 2014 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."