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This content explosion is to a large extent driven by a mix of novel technologies with a deep human drive for recognition. This talk will describe the regularities that govern how social attention is allocated among all media and the role it plays in the production and consumption of content.
It will also describe how its dynamics not only helps determine the emergence of public agendas but also be used to predict the evolution of social trends.
The many decisions people make about what information to attend to affect emerging trends, the diffusion of information in social media, and performance of crowds in peer evaluation tasks.
Due to constraints of available time and cognitive resources, the ease of discovery strongly affects how people allocate their attention. Through empirical analysis and online experiments, we identify some of the cognitive heuristics that influence individual decisions to allocate attention to online content and quantify their impact on individual and collective behavior.
Specifically, we show that the position of information in the user interface strongly affects whether it is seen, while explicit social signals about its popularity increase the likelihood of response.
These heuristics become even more important in explaining and predicting behavior as cognitive load increases. Trained as a physicist, she now applies network- and machine learning-based methods to problems in social computing and social media analysis.
Kristina Lerman , University of Southern California. For decades, physical behavioral labs have been a primary, yet limited, method for controlled experimental studies of human behavior. Now, software-based "virtual labs" on the Internet allow for studies of increasing complexity, size, and scope. In this talk, I highlight the potential of virtual lab experiments for studying social interaction and coordination. First, we explore collective intelligence and digital teamwork in "crisis mapping", where digital volunteers organize to assess and pinpoint damage in the aftermath of humanitarian crises.
By simulating a crisis mapping scenario to study self-organization in teams of varying size, and find a tradeoff between individual effort in small groups and collective coordination in larger teams. We also conduct a study of cooperation in a social dilemma over a month of real time, using crowdsourcing participants to overcome the time constraints of behavioral labs. Our study of about participants over 20 consecutive weekdays finds that a group of resilient altruists sustain a high level of cooperation across the entire population.
Together, our work motivates the potential of controlled, highly instrumented studies of social interaction; the importance of behavioral experiments on longer timescales; and how open-source software both can speed up the iteration and improve the reproducibility of experimental work. His research focuses on studying collective intelligence and social interaction on the Internet, such as teamwork in online communities and coordination in crowdsourcing systems.
Andrew specializes in designing and gathering data from real-time, interactive, web-based behavioral experiments, and he is the designer of TurkServer http: He received his PhD from Harvard University in Technology has advanced to a point where a large part of the population carries a mini-computer in their pockets that is disguised as a phone.
Their role has changed from simple communication devices to multi-functional information devices. They are packed with various sensors, such as GPS, gyroscopes, and accelerometers, which can collect contextual data of the device and the user. Thanks to a multitude of applications they satisfy a large spectrum of different needs, and stay with their users most of their time.
Smartphone usage is a hot topic in ubiquitous and pervasive computing due to their popularity and personal aspect. The Menthal team has developed the Menthal framework https: It is part of one of the largest in-the-wild smartphone studies. They attracted a large number of participants by running the study in a start-up format: From the launch of the project, in January , their app has been installed more than , times and their project attracted more than , registered participants.
The collected data allows to study a number of problems in HCI, psychological sciences and medicine. In this talk they will present their framework from a technical point of view and afterwards they will discuss past and current results from their research project. Ionut Andone from University of Bonn. The way we express ourselves is heavily influenced by our demographic background. Natural Language Processing NLP models, however, are based on a small demographic sample and approach all language as uniform.
As a result, NLP models perform worse on language from demographic groups that differ from the training data, i. This bias harms performance and can disadvantage entire user groups.
Sociolinguistics has long investigated the interplay of demographic factors and language use, and it seems likely that the same factors are also present in the data we use to train NLP systems.
In this talk, I will show how we can combine statistical NLP methods and sociolinguistic theories to the benefit of both fields.
I present ongoing research into large-scale statistical analysis of demographic language variation to detect factors that influence the performance and fairness of NLP systems, and how we can incorporate demographic information into statistical models to address both problems.
With increase in usage of the Internet, there has been an exponential increase in the use of online social media on the Internet. There is a dire need to investigate, measure, and understand privacy and security on online social media from various perspectives computational, cultural, psychological. Real world scalable systems need to be built to detect and defend security and privacy issues on online social media.
I will describe briefly some cool projects that we work on: Many of our research work is made available for public use through tools or online services. In particular, in this talk, I will focus on the following: TweetCred is backed by award-winning research publications in international and national venues.
Open source Collation of eGovernment data and Networks, how publicly available information on Government services can be used to profile citizens in India. This work is also backed by award-winning research publication. Research into socio-technical systems like Wikipedia has overlooked important structural patterns in the coordination of distributed work. This paper argues for a conceptual reorientation towards sequences as a fundamental unit of analysis for understanding work routines in online knowledge collaboration.
I outline a research agenda for computational social science researchers to understand the relationships, patterns, antecedents, and consequences of sequential behavior extending methods already developed in fields like sociology and bio-informatics. Using a data set of 37, revisions from 16, unique editors to 96 Wikipedia articles as a case study, we analyze the prevalence and significance of different sequences of editing patterns.
We illustrate the mixed method potential of sequence approaches by interpreting the frequent patterns as general classes of behavioral motifs. We conclude by discussing the methodological opportunities for using sequence analysis for expanding existing approaches to analyzing and theorizing about co-production routines in online knowledge collaboration.
Brian Keegan , Harvard Business School. Determining the relative centrality of actors, or the degree to which they are structurally important, is a most common technique in social network analysis. Many indices have been proposed to measure a variety of centrality conceptions, and choosing one that is most appropriate for the particular research question and data at hand proves a challenge in many empirical studies. We use a general result about all common centrality indices to motivate a re-conceptualization of centrality.
Our new approach is the first instantiation of a recently introduced positional framework for network analysis. By breaking down complex analytical into comprehensible steps, multivariate data and theoretical assumptions can be integrated more flexibly. Several examples serve to illustrate this point. Ulrik Brandes , University of Konstanz. Characterising how we explore abstract spaces is key to understand our ir rational behaviour and decision making.
While some light has been shed on the navigation of semantic networks, however, little is known about the mental exploration of metric spaces, such as the one dimensional line of numbers, prices, etc. Interestingly, this is the best strategy that can be adopted by a random searcher looking for a target in an unknown environment, and has been observed in the foraging patterns of many species. We also show that the histogram describing single individual exponents is well peaked, pointing out the existence of an almost universal behaviour.
Furthermore, a simple model reveals that the observed exponents are nearly optimal, and represent a Nash equilibrium. Our results show that humans share with other animals universal patterns in general searching processes, and raise fundamental issues in cognitive, behavioural and evolutionary sciences.
Human mobility has been a hot topic of interest for researchers due to its importance for many application scenarios that include nearby place search, mobile context awareness or mobile advertising. Despite the bevy of research on human mobility patterns analysis and prediction modeling of individual users, however, little attention has been put on the mobility patterns of user collectives across places in a city. In this talk, we will exploit network analysis techniques to view human movement in urban environments from the perspective of an aggregate networked system where nodes are Foursquare venues.
We will discuss the geometric properties of place networks in a large number of metropolitan areas around the world and how those compare to other well studied types of networks, such as on-line social networks or the web. Next, we will shed light on the growth patterns of place networks in terms of node and edge generation processes.
Motivated by the fact that a large number of new links is emerging over time in those networks, we will define a link prediction task in this novel application domain with the aim to predict future interactions between Foursquare venues. The talk will close by providing a head to head comparison over the prediction task amongst the well-known, in human mobility literature, gravity models, network-based techniques as well as supervised learning algorithms. Anastasios Noulas , University of Cambridge.
In the era of big data and social media analysis, as a way forward, I propose an alternative to vanity metrics or the quantification of trend and personal influence.
Rather, for the study of Twitter, Facebook and other secondary social media, I would like to put forward a critical data analytics that is sensitive to big data critique on the one hand and embraces analytical strategies with digital methods based on expertise and engagement on the other hand, making findings and outputting visualisations which are both insightful for ethical social research and aware of the hegemony of the graph.
Traditionally, most of football statistical and media coverage has been focused almost exclusively on goals and ocassionally shots. However, most of the duration of a football game is spent away from the boxes, passing the ball around.
The convergence of social and technical systems provides us with a wealth of data on the structure and dynamics of social organizations. It is tempting to utilize these data in order to better understand how social organizations evolve, how their structure is related to their "success", and how the position of individuals in the emerging social fabric affects their performance and motivation. Taking a complex network perspective on these questions, in this talk I will introduce recent research results obtained in the context of collaborative software engineering.
These results demonstrate the potential of network-based data mining methods in the study of social organizations. At the same time, I will highlight fallacies arising in the application of the complex networks perspective to social systems. It has become popular to tap into the "intelligence of the crowd" on the Internet.
This talk argues that more often than not, the crowd flips from intelligence to madness, showing more characteristics of football hooligans than complex problem solving behavior. This is in contrast to what I call "creative swarms", where small teams of intrinsically motivated people work together in Collaborative Innovation Networks COINs to invent something radically new.
The key difference is in motivation: The variables are computed by analyzing global communication on the Web, in Twitter, and Wikipedia, in organizations through e-mail, and in small teams through sociometric badges.
The talk is illustrated by many examples, with emphasis on high-tech firms and healthcare. It also introduces the Chronic Collaborative Care Network C3N at Cincinnati Children's Hospital, where COINs of medical researchers, physicians, patients and their families are working together to improve the lives of patients with Crohn's disease, diabetes, and cystic fibrosis.
Query-specific Wikipedia Construction We all turn towards Wikipedia with questions we want to know more about, but eventually find ourselves on the limit of its coverage. Instead of providing "ten blue links" like common in Web search, my goal is to answer any web query with something that looks and feels like Wikipedia.
I am developing algorithms to automatically retrieve, extract, and compile a knowledge resource for a given web query. I will talk about a supervised retrieval model that can jointly identify relevant Web documents, Wikipedia entities, and extract support passages. Network Topic Models Topic models such as Latent Dirichlet Allocation are an unsupervised technique to extract word clusters with topical character from a given corpus of text documents. Often we find text documents with an underlying link structure, or a network in which nodes are associated with text content.
It is often assumed that connected nodes have some shared trait or interest which motivated the forming of the connection. In this talk, I will discuss several topic model extensions for textual network data.
This includes the Citation Influence Model  which quantifies the strengths of a citation strength in an acyclic graph through a topic model. Furthermore, I will discuss the Shared Taste Model  which learns topics that capture shared interests in an undirected social network.
As communication between users is often off-limits due to privacy concerns, the model learns from public text written by users, such as tweets, tags, posts, etc.
The goal is to predict which friend of the user is interested in the content. The source code for both models is available on Github. Improving Website Hyperlink Structure Using Server Logs Good websites should be easy to navigate via hyperlinks, yet maintaining a link structure of high quality is difficult.
Identifying pairs of pages that should be linked may be hard for human editors, especially if the site is large and changes are frequent.
We show that passively collected server logs, beyond telling us which existing links are useful, also contain implicit signals indicating which nonexistent links would be useful if they were to be introduced. We leverage these signals to model the future usefulness of as yet nonexistent links.
Based on our model, we define the problem of link placement under budget constraints and propose an efficient algorithm for solving it. We demonstrate the effectiveness of our approach by evaluating it on Wikipedia and Simtk. In particular, it expressed concerns about the possibility that decisions informed by big data could have discriminatory effects, even in the absence of discriminatory intent, further imposing less favorable treatment to already disadvantaged groups.
In its recommendations to the President, the report called for additional "technical expertise to stop discrimination", and for further research into the dangers of "encoding discrimination in automated decisions". In parallel to development in anti-discrimination legislation, efforts at fighting discrimination have led to developing anti-discrimination techniques in data mining.
Some proposals are oriented to the discovery and measurement of discrimination, while others deal with preventing data mining from becoming itself a source of discrimination, due to automated decision making based on discriminatory models extracted from inherently biased datasets. In this talk, I will introduce some of the recent techniques for discrimination prevention, simultaneous discrimination and privacy protection, and discrimination discovery and show some recent results.
Sara Hajian , Eurecat-Technology center of Catalonia. Due to the low acquisition cost and its sheer scale, social media is becoming a popular data source for studies on tracking health trends at scale.
In this talk, I will present recent and ongoing work that uses social media data to study lifestyle diseases such as obesity. The first line of work takes the population-centric approach and uses food mentions on Twitter to study obesity.
I will then move on towards individual-centric health studies of obesity and dieting. I argue that it is rather the beginning, due to new opportunities for a relational theoretical framework. From astronomy to neuroscience to particle physics, scientific knowledge depends decisively on the available tools for observation. For the past century or more , the survey has been the single most important observational tool for social science. During this time, enormous advances have taken place in our ability to reduce systematic bias in sampling hidden populations, to reduce measurement error in the responses to survey items, to reduce statistical error in the analysis of results, and to reduce inferential error in causal models of the associations among the measures.
Nevertheless, increasing confidence in survey technology has paradoxically reinforced a debilitating theoretical blinder that has compromised the ability of social science to elicit confidence in predictions. What is worse, this blinder has largely escaped notice through a combination of ideological bias and reluctance to pull back the covers on problems for which we have no solution.
The good news is that a solution is finally on the horizon. The personal stories that people post to their public weblogs offer a glimpse into the everyday lives of people.
In this talk I will discuss our efforts to automatically gather tens of millions of these stories, and use them as a dataset for investigating different populations of authors.
I will discuss our work on analyzing the stories that people tell about health emergencies strokes , and how this led us to concerns about sample bias.
I will describe our ongoing work on bias correction for social-media samples, and discuss opportunities afforded by populations of extremely prolific webloggers whose demographic information can be readily extracted from the stories they share. With the widespread adoption of social media sites like Twitter and Facebook, there has been a shift in the way information is produced and consumed in our societies. Traditionally, information was produced by large news organizations, which broadcast the same carefully-edited information to all consumers over mass media channels.
In contrast, in online social media, any user can be a producer of information, and every user selects which other users she connects to, thereby choosing the information she consumes. Furthermore, recommender systems deployed on most social media sites provide users with additional information that is tailored to their individual tastes.
In this talk, I will introduce the concept of information diet — which is the topical or distribution of a given set of information items e. At a high level, we find that i popular users mostly produce very specialized diets focusing on only a few topics; in fact, news organizations e. Axel Philipps , Leibniz University of Hannover.
Wikipedia is a huge global repository of human knowledge, and at the same time one of the largest experiments of online collaboration. Its articles, their links and the negotiations around their content tend to reflect societal debates in different language communities. The first work I will present is Contropedia, a platform that adds a layer of transparency to Wikipedia articles.
Combining activity from the edit history and discussion in talk pages, the platform uses wiki links as focal points to explore the development of controversial issues over time. The second study focuses on the network of hyperlinks in different language editions of Wikipedia. A ranking of the most central biographies in each language edition is used to study relationships and influences between cultures.
Finally, I will present a large-scale analysis of emotional expression and communication style of editors in Wikipedia discussions, focusing on how emotion and dialogue differ depending on the editors' status, gender, and communication network.
Modeling social media and blogging is a fashionable topic of research. The challenges include how to model, exploit, store, and analyze social content data. In this talk we will discuss about novel approaches on how to exploit such data in order to build: Since s urban theories proposed by Lynch and Milgram aimed at understanding complex city dynamics.
Can these theories be put to use for enabling new mobile services? Existing mapping technologies return shortest directions. To complement them, we are designing new mobile phone tools that return directions that not only are short but also make the experience of pedestrians happier. To capture a fuzzy concept such as happiness, we have combined Flickr metadata with the crowdsourcing site urbangems. This crowdsources visual perceptions of quiet, beauty and happiness across the city of London using pictures of street scenes.
Daniele Quercia , Yahoo Labs Barcelona. The problem of understanding the dynamics of collective attention has been identified as a key scientific challenge for the information age. In this talk, we first show that search behaviors of large populations of Internet users evolve in a highly regular manner and that corresponding time series can be modeled using skewed distributions.
We then ask if such dynamics could be explained in terms of infectious processes that take place in social networks and derive a physically plausible, model for the temporal dynamics of graph diffusion processes. Our results are based on maximum entropy arguments and provide new approaches to problems in network analysis and mining. Our first study focuses on Politnetz, a Swiss platform focused on political activity, composed of support links between politicians, comments, and likes.
We found that comment structures follow topics related to Swiss politics, and that polarization in likes evolves in time, increasing when the federal elections of were close. Furthermore, we analyzed the internal social structure of each party through social network metrics related to hierarchical structures and information efficiency.
This analysis highlights patters of the relation between the connectivity patterns of parties and their political position within a multi-party system. Our second work analyzes the evolution of the 15M movement through its digital traces in the Twitter social network.
We analyzed the tweets related to the movement during 30 days around its creation, providing an illustration of the evolution and structure of the movement at its collective and individual level. We found patterns of influence of collective action and mass media in the polarization of opinions about the movement, and found different stages of movement formation and expansion through Twitter activity.
Our sentiment and psycholinguistic analysis of the content of tweets reveals that activity cascades with strong negative sentiment and social-related terms spread to larger amounts of users. At the individual level, we found that users that are more embedded in the movement display higher levels of activity and express stronger negativity, in line with the overall negative context of the movement.
Though online social network research has exploded during the past years, not much thought has been given to the exploration of the nature of the social structures that compose them. Online interactions have been interpreted as indicative of one social process or another e. Our research aims to breach this gap in computational social science by trying to explain the nature and purpose of social structures, with quantitative metrics that are directly derived from longstanding concepts in social sciences.
In this talk we will discuss about characterization of social links and social groups. We propose a method based on Blau's notion of resource exchange that discovers, with high accuracy, the fundamental domains of interaction occurring over links in social networks. Please don't fill out this field. There are no minimum. Start Trading Binary Options with Confidence. A binary option is a financial option in which the payoff is either some fixed monetary amount or nothing at all.
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