[Conference News] Extracting Hidden Behavioral Patterns from Social Network Data

Massive information about human behavior is continuously generated by Web-based services, both public and private. The data include traces of not only individual activities but also collaborative work, and the social networks that can be extracted from these datasets offer a kind of knowledge that’s independent of user awareness.

In a paper presented at the 2013 International Conference on Social Intelligence and Technology (Social 2013), researchers from the Wroclaw University of Technology in Poland describe a data-driven approach to social network analysis that enables various applications of knowledge about human behavior. They illustrate selected models and analytical methods in applications to recommender systems, organizational structure analysis, and social group evolution.

“From Data to Human Behaviour” and other papers from Social 2013 are available to both IEEE Computer Society members and paid subscribers via the Computer Society Digital Library.

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Eating or spending too much? Blame it on social networking sites

Dec. 11, 2012 — Participating in online social networks can have a detrimental effect on consumer well-being by lowering self-control among certain users, according to a new study in the Journal of Consumer Research.

"Using online social networks can have a positive effect on self-esteem and well-being. However, these increased feelings of self-worth can have a detrimental effect on behavior. Because consumers care about the image they present to close friends, social network use enhances self-esteem in users who are focused on close friends while browsing their social network. This momentary increase in self-esteem leads them to display less self-control after browsing a social network," write authors Keith Wilcox (Columbia University) and Andrew T. Stephen (University of Pittsburgh).

Online social networks are having a fundamental impact on society. Facebook, the largest, has over one billion active users. Does using a social network impact the choices consumers make in their daily lives? If so, what effect does it have on consumer well-being?

A series of interesting studies showed that Facebook usage lowers self-control for consumers who focus on close friends while browsing their social network. Specifically, consumers focused on close friends are more likely to choose an unhealthy snack after browsing Facebook due to enhanced self-esteem. Greater Facebook use was associated with a higher body-mass index, increased binge eating, a lower credit score, and higher levels of credit card debt for consumers with many close friends in their social network.

"These results are concerning given the increased time people spend using social networks, as well as the worldwide proliferation of access to social networks anywhere anytime via smartphones and other gadgets. Given that self-control is important for maintaining social order and personal well-being, this subtle effect could have widespread impact. This is particularly true for adolescents and young adults who are the heaviest users of social networks and have grown up using social networks as a normal part of their daily lives," the authors conclude.

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Journal Reference:

Keith Wilcox, Andrew T. Stephen. Are Close Friends the Enemy? Online Social Networks, Self-Esteem, and Self-Control. Journal of Consumer Research, 2012; : 000 DOI: 10.1086/668794

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Who’s the most influential in a social graph? New software recognizes key influencers faster than ever

ScienceDaily (Sep. 7, 2012) — At an airport, many people are essential for planes to take off. Gate staffs, refueling crews, flight attendants and pilots are in constant communication with each other as they perform required tasks. But it's the air traffic controller who talks with every plane, coordinating departures and runways. Communication must run through her in order for an airport to run smoothly and safely.

In computational terms, the air traffic controller is the "betweenness centrality," the most connected person in the system. In this example, finding the key influencer is easy because each departure process is nearly the same.

Determining the most influential person on a social media network (or, in computer terms, a graph) is more complex. Thousands of users are interacting about a single subject at the same time. New people (known computationally as edges) are constantly joining the streaming conversation.

Georgia Tech has developed a new algorithm that quickly determines betweenness centrality for streaming graphs. The algorithm can identify influencers as information changes within a network. The first-of-its-kind streaming tool was presented this week by Computational Science and Engineering Ph.D. candidate Oded Green at the Social Computing Conference in Amsterdam.

"Unlike existing algorithms, our system doesn't restart the computational process from scratch each time a new edge is inserted into a graph," said College of Computing Professor David Bader, the project's leader. "Rather than starting over, our algorithm stores the graph's prior centrality data and only does the bare minimal computations affected by the inserted edges."

In some cases, betweenness centrality can be computed more than 100 times faster using the Georgia Tech software. The open source software will soon be available to businesses.

Bader, the Institute's executive director for high performance computing, says the technology has wide-ranging applications. For instance, advertisers could use the software to identify which celebrities are most influential on Twitter or Facebook, or both, during product launches.

"Despite a fragmented social media landscape, data analysts would be able to use the algorithm to look at each social media network and mark inferences about a single influencer across these different platforms," said Bader.

As another example, the algorithm could be used for traffic patterns during a wreck or traffic jam. Transportation officials could quickly determine the best new routes based on gradual side-street congestion.

The accepted paper was co-authored by Electrical and Computer Engineering Ph.D. candidate Rob McColl.

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Math tree may help root out fraudsters: Applying algorithm to social networks can reveal hidden connections criminals use to commit fraud

ScienceDaily (Sep. 5, 2012) — Fraudsters beware: the more your social networks connect you and your accomplices to the crime, the easier it will be to shake you from the tree.

The Steiner tree, that is.

In an article recently published in the journal Computer Fraud and Security, University of Alberta researcher Ray Patterson and colleagues from the University of Connecticut and University of California -- Merced outlined the connection linking fraud cases and the algorithm designed by Swiss mathematician Jakob Steiner. Fraud is a problem that costs Canadians billions of dollars annually and countless hours of police investigations. Patterson says that building the algorithm into fraud investigation software may provide important strategic advantages.

The criminal path of least resistance

To quote a television gumshoe, everything's connected. Figuring out who knows who and who has access to the money is like playing a game of connect-the-dots. Patterson says that for crimes like fraud, the fewer players in the scheme, the more likely it will be accomplished. Maintaining a small group of players is also what links it to the Steiner tree. He says that by analyzing various connecting social networks -- email, Facebook or the like -- finding out the who, what and how of the crime can be boiled down to numbers.

"You're really trying to find the minimum set of connectors that connect these people to the various [network] resources," he said. "The minimum number of people required is what's most likely to be the smoking gun. You can do it with math, once you know what the networks are."

Fraud and the Steiner tree, by the numbers

In their article, Patterson and his colleagues explored how networks such as phone calls, business partnerships and family relationships are used to form essential relationships in a fraud investigation. When these same relationships are layered, a pattern of connection becomes obvious. Once unnecessary links are removed and false leads are extracted, the remaining connections are most likely the best suspects. Patterson says that finding the shortest connection between the criminals and the crime is the crux of the Steiner tree.

"All of these things that we see in life, behind them is a mathematical representation," said Patterson. "There are many, many different algorithms that we can pull off a shelf and apply to real-life problems."

A potential tool for the long arm of the law?

Patterson says that with the amount of work that could potentially go into investigating a fraud case, such as obtaining warrants for phone or email records, and identifying and interviewing potential suspects, developing a program that uses a Steiner tree algorithm may save a significant portion of investigators' time -- time that, he says, could likely be reallocated to backlog or cold case files. "If you can reduce your legwork by even 20 per cent, that has massive manpower implications. I think algorithms like this one could help you reduce your legwork a lot more than that," he said.

Although there is software that police and other law enforcement agencies can use to solve fraud, Patterson sees no evidence that those programs use a Steiner tree algorithm, something he says would bring some structure to an unstructured area. He hopes programmers and investigators will take note of the findings and make changes to their practices.

"It might take several years or many years before anyone picks it up," said Patterson. "But it's a good thing if we can point people towards what's useful."

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Journal Reference:

Ram D Gopal, Raymond A Patterson, Erik Rolland, Dmitry Zhdanov. Social network meets Sherlock Holmes: investigating the missing links of fraud. Computer Fraud & Security, 2012; 2012 (7): 12 DOI: 10.1016/S1361-3723(12)70074-X

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Search engine for social networks based on the behavior of ants

ScienceDaily (June 4, 2012) — Research at Carlos III University (Universidad Carlos III) in Madrid (Universidad Carlos III -- UC3M) is developing an algorithm, based on ants' behavior when they are searching for food, which accelerates the search for relationships among elements that are present in social networks.

One of the main technical questions in the field of social networks, whose use is becoming more and more generalized, consists in locating the chain of reference that leads from one person to another, from one node to another. The greatest challenges that are presented in this area is the enormous size of these networks and the fact that the response must be rapid, given that the final user expects results in the shortest time possible. In order to find a solution to this problem, these researchers from UC3M have developed an algorithm SoSACO, which accelerates the search for routes between two nodes that belong to a graph that represents a social network.

The way SoSACO works was inspired by behavior that has been perfected over thousands of years by one of the most disciplined insects on the planet when they search for food. In general, the algorithms used by colonies of ants imitate how they are capable of finding the path between the anthill and the source of food by secreting and following a chemical trail, called a pheromone, which is deposited on the ground.

"In this study -- the authors explain -- other scented trails are also included so that the ants can follow both the pheromone as well as the scent of the food, which allows them to find the food source much more quickly." The main results of this research, which was carried out by Jessica Rivero in UC3M's Laboratorio de Bases de Datos Avanzadas (The Advanced Data Bases Laboratory -- LABDA) as part of her doctoral thesis, are summarized in a scientific article published in the journal Applied Intelligence. "The early results show that the application of this algorithm to real social networks obtains an optimal response in a very short time (tens of milliseconds)," Jessica Rivero states.

Multiple applications

Thanks to this new search algorithm, the system can find these routes more easily, and without modifying the structure of graph (an image that uses nodes and links to represents the relationships among a set of elements). "This advance allows us to solve many problems that we find in the real world, because the scenarios in which they occur can be modeled by a graph," the researchers explain. Thus, it could be applied in many different scenarios, such as to improve locating routes in FPS systems or in on-line games, to plan deliveries for freight trucks, to know if two words are somehow related or to simply know exactly which affinities two Facebook or Twitter users, for example, have in common.

This research, which has received support from the Autonomous Community of Madrid (MA2VICMR, S2009/TIC-1542) and the Ministry of Education and Science (Ministerio de Educación y Ciencia), began as part of the SOPAT project (TSI-020110-2009-419), in response to the need to guide a hotel's clients using a natural interaction system. Jessica Rivero's doctoral thesis, which deals with this subject, is titled "Búsqueda Rápida de Caminos en Grafos de Alta Cardinalidad Estáticos y Dinámicos" ("A Quick Search for Routes in Static and Dynamic Graphs of High Cardinality"); it was directed by Francisco Javier Calle y Mª Dolores Cuadra, professors in the LABDA of the Computer Science Department, and received a grade of Apto-Cum Laude (Pass-Cum Laude).

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Search engine for social networks based on the behavior of ants

ScienceDaily (June 4, 2012) — Research at Carlos III University (Universidad Carlos III) in Madrid (Universidad Carlos III -- UC3M) is developing an algorithm, based on ants' behavior when they are searching for food, which accelerates the search for relationships among elements that are present in social networks.

One of the main technical questions in the field of social networks, whose use is becoming more and more generalized, consists in locating the chain of reference that leads from one person to another, from one node to another. The greatest challenges that are presented in this area is the enormous size of these networks and the fact that the response must be rapid, given that the final user expects results in the shortest time possible. In order to find a solution to this problem, these researchers from UC3M have developed an algorithm SoSACO, which accelerates the search for routes between two nodes that belong to a graph that represents a social network.

The way SoSACO works was inspired by behavior that has been perfected over thousands of years by one of the most disciplined insects on the planet when they search for food. In general, the algorithms used by colonies of ants imitate how they are capable of finding the path between the anthill and the source of food by secreting and following a chemical trail, called a pheromone, which is deposited on the ground.

"In this study -- the authors explain -- other scented trails are also included so that the ants can follow both the pheromone as well as the scent of the food, which allows them to find the food source much more quickly." The main results of this research, which was carried out by Jessica Rivero in UC3M's Laboratorio de Bases de Datos Avanzadas (The Advanced Data Bases Laboratory -- LABDA) as part of her doctoral thesis, are summarized in a scientific article published in the journal Applied Intelligence. "The early results show that the application of this algorithm to real social networks obtains an optimal response in a very short time (tens of milliseconds)," Jessica Rivero states.

Multiple applications

Thanks to this new search algorithm, the system can find these routes more easily, and without modifying the structure of graph (an image that uses nodes and links to represents the relationships among a set of elements). "This advance allows us to solve many problems that we find in the real world, because the scenarios in which they occur can be modeled by a graph," the researchers explain. Thus, it could be applied in many different scenarios, such as to improve locating routes in FPS systems or in on-line games, to plan deliveries for freight trucks, to know if two words are somehow related or to simply know exactly which affinities two Facebook or Twitter users, for example, have in common.

This research, which has received support from the Autonomous Community of Madrid (MA2VICMR, S2009/TIC-1542) and the Ministry of Education and Science (Ministerio de Educación y Ciencia), began as part of the SOPAT project (TSI-020110-2009-419), in response to the need to guide a hotel's clients using a natural interaction system. Jessica Rivero's doctoral thesis, which deals with this subject, is titled "Búsqueda Rápida de Caminos en Grafos de Alta Cardinalidad Estáticos y Dinámicos" ("A Quick Search for Routes in Static and Dynamic Graphs of High Cardinality"); it was directed by Francisco Javier Calle y Mª Dolores Cuadra, professors in the LABDA of the Computer Science Department, and received a grade of Apto-Cum Laude (Pass-Cum Laude).

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National study ranks city governments' use of social media

ScienceDaily (Mar. 22, 2012) — More than six times as many big city governments reached citizens via Facebook in 2011 compared to 2009, while use of YouTube and Twitter grew fourfold and threefold respectively, a new study indicates.

Karen Mossberger, head of the University of Illinois at Chicago's public administration graduate program, and Yonghong Wu, associate professor, analyzed and ranked the online interactivity, transparency and accessibility of the country's 75 largest cities from March through May 2011. They used the data to compile the Civic Engagement Index, and compared it with their findings from a study they conducted in 2009.

The cities' rankings reflected opportunities for citizen participation and information, including: -- hosting of open data portals -- comments allowed on blogs and social networks -- the extent to which online discussions concerned policy as well as city services -- information on officials, budgets, city council meetings and neighborhood issues

New York and Seattle tied for first place, followed by Virginia Beach, Va.; Portland, Ore.; San Francisco; and Kansas City, Mo., the study reported.

Mossberger said the top-ranked city governments have made technology a priority, especially for transparency or civic engagement.

"Seattle has long been an innovator in this area, with programs to address the digital divide online and offline. New York has long used the web for transparency," she said.

Chicago tied with San Diego and Minneapolis at 17th. Toledo ranked last.

The complete rankings may be seen at http://www.uic.edu/cuppa/ipce/research.shtml.

Twitter was used by 87 percent of the cities, compared with 25 percent in 2009. Facebook also was used by 87 percent of the cities, up from 13 percent. YouTube links appeared on the websites of 75 percent of the cities, up from 16 percent.

Nearly all city sites allowed comments on Twitter, Facebook and YouTube and presented policy content such as discussions of city budgets.

"In Chicago, for example, the Emanuel administration solicited budget ideas last summer on Twitter," Mossberger said. "Louisville Mayor Greg Fischer regularly holds a virtual 'Talk to Greg' on Facebook and Twitter. Seattle is experimenting with platforms like the IdeaScale, where users can submit and rate ideas."

Open data portals were found in only 12 cities: Baltimore, Boston, Chicago, Honolulu, Louisville, Ky., Milwaukee, New York, Philadelphia, Portland, Ore., San Francisco, Seattle, and Washington, D.C. The portals allow users access to city data on crime, budgets, Freedom of Information Act requests, city facilities, vacant land, building permits and other matters.

The researchers note that some information is not formatted for easy use by average citizens.

"For example, cities often post files that require special software, such as geographic information system software. And budget data can be difficult for citizens to understand," Mossberger said.

Mossberger predicts that new apps may make information on data portals more usable. Apps designed in competitions in Chicago, New York, and Washington, D.C. have focused on civic engagement as well as city services.

"First-place winners in New York and Chicago addressed traffic and parking," Mossberger said. "But in Chicago, another winning app allowed residents to contribute ideas for a park. Other apps have been designed to track lobbyists in Chicago."

In another study confined to Illinois' 20 largest cities, the researchers found that 55 percent of the cities used Twitter, Facebook and YouTube in 2011, compared to 15 percent for Twitter, 10 percent for Facebook, and 10 percent for YouTube in 2009.

"Ultimately, the impact of these tools depends on factors other than technology -- the quality of the information, local government practices and citizen response," Mossberger said.

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