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arXiv2014

Reverse Engineering Socialbot Infiltration Strategies in Twitter

Data extracted from social networks like Twitter are increasingly being used to build applications and services that mine and summarize public reactions to events, such as traffic monitoring platforms, identification of epidemic outbreaks, and public perception about people and brands. However, such services are vulnerable to attacks from socialbots $-$ automated accounts that mimic real users $-$ seeking to tamper statistics by posting messages generated automatically and interacting with legitimate users. Potentially, if created in large scale, socialbots could be used to bias or even invalidate many existing services, by infiltrating the social networks and acquiring trust of other users with time. This study aims at understanding infiltration strategies of socialbots in the Twitter microblogging platform. To this end, we create 120 socialbot accounts with different characteristics and strategies (e.g., gender specified in the profile, how active they are, the method used to generate their tweets, and the group of users they interact with), and investigate the extent to which these bots are able to infiltrate the Twitter social network. Our results show that even socialbots employing simple automated mechanisms are able to successfully infiltrate the network. Additionally, using a $2^k$ factorial design, we quantify infiltration effectiveness of different bot strategies. Our analysis unveils findings that are key for the design of detection and counter measurements approaches.

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arXiv2014

Competitive Dynamics on Complex Networks

We consider a dynamical network model in which two competitors have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. The state of each normal agent converges to a steady value which is a convex combination of the competitors' states, and is independent of the initial states of agents. This implies that the competition result is fully determined by the network structure and positions of competitors in the network. We compute an Influence Matrix (IM) in which each element characterizing the influence of an agent on another agent in the network. We use the IM to predict the bias of each normal agent and thus predict which competitor will win. Furthermore, we compare the IM criterion with seven node centrality measures to predict the winner. We find that the competitor with higher Katz Centrality in an undirected network or higher PageRank in a directed network is much more likely to be the winner. These findings may shed new light on the role of network structure in competition and to what extent could competitors adjust network structure so as to win the competition.

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arXiv2014

Indian Premier League (IPL), Cricket, Online Social Media

In recent past online social media has played a pivotal role in sharing of information and opinions on real time events. Events in physical space are reflected in digital world through online social networks. Event based studies for such content have been widely done on Twitter in the computer science community. In this report, we performed a detailed analysis of a sports event called the Indian Premier League (IPL'13) for both Facebook and Twitter. IPL is the most popular cricket league in India with players from across the world. We analysed more than 2.6 million tweets and 700 thousand Facebook posts for temporal activity, text quality, geography of users and the spot-fixing scandal which came up during the league. We were able to draw strong correlations between the brand value of teams and how much they were talked about on social media across Facebook and Twitter. Analysis of geo-tagged data showed major activity from metropolitan suburbs however activity was not restricted to the regions geographically associated with each team. We present a decay calculation methodology, using which we derive that activity died down on both Twitter and Facebook in a very similar manner. Such analysis can be used to model events and study their penetration in social networks. We analysed text for spot-fixing and found that user response to allegations about matches being fixed was cold. The complete analysis presented in this report, can be particularly useful for studying events involving crisis or events of political importance having similar structure.

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arXiv2014

Design of Efficient Sampling Methods on Hybrid Social-Affiliation Networks

Graph sampling via crawling has become increasingly popular and important in the study of measuring various characteristics of large scale complex networks. While powerful, it is known to be challenging when the graph is loosely connected or disconnected which slows down the convergence of random walks and can cause poor estimation accuracy. In this work, we observe that the graph under study, or called target graph, usually does not exist in isolation. In many situations, the target graph is related to an auxiliary graph and an affiliation graph, and the target graph becomes well connected when we view it from the perspective of these three graphs together, or called a hybrid social-affiliation graph in this paper. When directly sampling the target graph is difficult or inefficient, we can indirectly sample it efficiently with the assistances of the other two graphs. We design three sampling methods on such a hybrid social-affiliation network. Experiments conducted on both synthetic and real datasets demonstrate the effectiveness of our proposed methods.

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arXiv2014

Ranking Swing Voters in Congressional Elections

We present a model for quantitatively identifying swing voters in congressional elections. This is achieved by predicting an individual voter's likelihood to vote and an individual voter's likelihood to vote for a given party, if he votes. We make a rough prediction of these values. We then update these predictions by incorporating information on a municipality wide basis via aggregate data to enhance our estimate under the assumption that nearby voters have similar behavior, which could be due to social interaction or common external factors. Finally, we use a ranking scheme on these predictions to identify two key types of voter: 1) Voters who are likely to vote that we can convince to vote for a given party; and, 2) Voters who are likely to vote for a given party, if they vote, that we can convince to actually turn out to vote. Once these voters have been identified, a political campaign can use this information to micro-target voters and win more votes.

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arXiv2014

Recommending Targeted Strangers from Whom to Solicit Information on Social Media

We present an intelligent, crowd-powered information collection system that automatically identifies and asks target-ed strangers on Twitter for desired information (e.g., cur-rent wait time at a nightclub). Our work includes three parts. First, we identify a set of features that characterize ones willingness and readiness to respond based on their exhibited social behavior, including the content of their tweets and social interaction patterns. Second, we use the identified features to build a statistical model that predicts ones likelihood to respond to information solicitations. Third, we develop a recommendation algorithm that selects a set of targeted strangers using the probabilities computed by our statistical model with the goal to maximize the over-all response rate. Our experiments, including several in the real world, demonstrate the effectiveness of our work.

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arXiv2014

Big Data, Socio-Psychological Theory, Algorithmic Text Analysis and Predicting the Michigan Consumer Sentiment Index

We describe an exercise of using Big Data to predict the Michigan Consumer Sentiment Index, a widely used indicator of the state of confidence in the US economy. We carry out the exercise from a pure ex ante perspective. We use the methodology of algorithmic text analysis of an archive of brokers' reports over the period June 2010 through June 2013. The search is directed by the social-psychological theory of agent behaviour, namely conviction narrative theory. We compare one month ahead forecasts generated this way over a 15 month period with the forecasts reported for the consensus predictions of Wall Street economists. The former give much more accurate predictions, getting the direction of change correct on 12 of the 15 occasions compared to only 7 for the consensus predictions. We show that the approach retains significant predictive power even over a four month ahead horizon.

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arXiv2014

Towards Cost-efficient Sampling Methods

The sampling method has been paid much attention in the field of complex network in general and statistical physics in particular. This paper presents two new sampling methods based on the perspective that a small part of vertices with high node degree can possess the most structure information of a network. The two proposed sampling methods are efficient in sampling the nodes with high degree. The first new sampling method is improved on the basis of the stratified random sampling method and selects the high degree nodes with higher probability by classifying the nodes according to their degree distribution. The second sampling method improves the existing snowball sampling method so that it enables to sample the targeted nodes selectively in every sampling step. Besides, the two proposed sampling methods not only sample the nodes but also pick the edges directly connected to these nodes. In order to demonstrate the two methods' availability and accuracy, we compare them with the existing sampling methods in three commonly used simulation networks that are scale-free network, random network, small-world network, and two real networks. The experimental results show that the two proposed sampling methods perform much better than the compared existing sampling methods in terms of sampling cost and obtaining the true network structural characteristics.

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arXiv2014

Case study to approaches to finding patterns in citation networks

Analysis of a dataset including a network of LED patents and their metadata is carried out using several methods in order to answer questions about the domain. We are interested in finding the relationship between the metadata and the network structure; for example, are central patents in the network produced by larger or smaller companies? We begin by exploring the structure of the network without any metadata, applying known techniques in citation analysis and a simple clustering scheme. These techinques are then combined with metadata analysis to draw preliminary conclusions about the dataset.

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arXiv2014

Prediction of Foreign Box Office Revenues Based on Wikipedia Page Activity

A number of attempts have been made at estimating the amount of box office revenue a film will generate during its opening weekend. One such attempt makes extensive use of the number of views a film's Wikipedia page has attracted as a predictor of box office success in the United States. In this paper we develop a similar method of approximating box office success. We test our method using 325 films from the United States and then apply it to films from four foreign markets: Japan (95 films), Australia (118 films), Germany (105 films), and the United Kingdom (141 films). We find the technique to have inconsistent performance in these nations. While it makes relatively accurate predictions for the United States and Australia, its predictions in the remaining markets are not accurate enough to be useful.

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arXiv2014

How to Control a Decrease in Physics Enrollment?

Physics community is generally concerned about the decrease in Physics enrollment. Due to the deficiency of specially qualified Physics teachers, high school Physics is sometimes taught by general science teachers who may not be trained to motivate students to study Physics. These students will neither be inclined to register into Physics courses nor plan to teach Physics. They will be unable to find out the relevance of Physics with daily life, its application in different disciplines and even the job market. In this situation, we have to carefully make the existing Physics programs more attractive, instead of closing them down. We discuss teaching methodology and course requirements that will help to make Physics programs more attractive and preferable for incoming students. However, we emphasize to set our goals and plan to increase enrollment in parallel steps such as proper information about the program, help in developing a required mathematics background, offering scholarships, teaching assistantship or internships and involving them in research. Also we need to make Physics programs more interesting with the interdisciplinary courses and other electives which students may like. We will have to work on the retention rate to maintain enrollment without compromising on standards. However, we still have to develop a complete understanding of the problem and keep looking for a better solution.

physics.ed-ph↗

arXiv2014

@I to @Me: An Anatomy of Username Changing Behavior on Twitter

An identity of a user on an online social network (OSN) is defined by her profile, content and network attributes. OSNs allow users to change their online attributes with time, to reflect changes in their real-life. Temporal changes in users' content and network attributes have been well studied in literature, however little research has explored temporal changes in profile attributes of online users. This work makes the first attempt to study changes to a unique profile attribute of a user - username and on a popular OSN which allows users to change usernames multiple times - Twitter. We collect, monitor and analyze 8.7 million Twitter users at macroscopic level and 10,000 users at microscopic level to understand username changing behavior. We find that around 10% of monitored Twitter users opt to change usernames for possible reasons such as space gain, followers gain, and username promotion. Few users switch back to any of their past usernames, however prefer recently dropped usernames to switch back to. Users who change usernames are more active and popular than users who don't. In-degree, activity and account creation year of users are weakly correlated with their frequency of username change. We believe that past usernames of a user and their associated benefits inferred from the past, can help Twitter to suggest its users a set of suitable usernames to change to. Past usernames may also help in other applications such as searching and linking multiple OSN accounts of a user and correlating multiple Twitter profiles to a single user.

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arXiv2014

When Politicians Talk: Assessing Online Conversational Practices of Political Parties on Twitter

Assessing political conversations in social media requires a deeper understanding of the underlying practices and styles that drive these conversations. In this paper, we present a computational approach for assessing online conversational practices of political parties. Following a deductive approach, we devise a number of quantitative measures from a discussion of theoretical constructs in sociological theory. The resulting measures make different - mostly qualitative - aspects of online conversational practices amenable to computation. We evaluate our computational approach by applying it in a case study. In particular, we study online conversational practices of German politicians on Twitter during the German federal election 2013. We find that political parties share some interesting patterns of behavior, but also exhibit some unique and interesting idiosyncrasies. Our work sheds light on (i) how complex cultural phenomena such as online conversational practices are amenable to quantification and (ii) the way social media such as Twitter are utilized by political parties.

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arXiv2014

Modeling Terrorist Networks, Complex Systems at the Mid-range

In this paper, we develop the themes presented at the 2003 Joint Complexity Conference at the London School of Economics and subsequently published in The Intelligencer (2004) and O Tempo Das Redes (2008). Following the data analysis of the 9/11 high-jacker network developed by Valdis Krebs from open sources, we apply social network theory to examine salient arguments regarding terrorism as seen from the standpoint of complex adaptive systems theory. In particular, we explore the concepts of group cohesion, adhesion and alternative network mappings derived from node removal.

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arXiv2014

Influence Spread in Social Networks: A Study via a Fluid Limit of the Linear Threshold Model

Threshold based models have been widely used in characterizing collective behavior on social networks. An individual's threshold indicates the minimum level of influence that must be exerted, by other members of the population engaged in some activity, before the individual will join the activity. In this work, we begin with a homogeneous version of the Linear Threshold model proposed by Kempe et al. in the context of viral marketing, and generalize this model to arbitrary threshold distributions. We show that the evolution can be modeled as a discrete time Markov chain, and, by using a certain scaling, we obtain a fluid limit that provides an ordinary differential equation model (o.d.e.). We find that the threshold distribution appears in the o.d.e. via its hazard rate function. We demonstrate the accuracy of the o.d.e. approximation and derive explicit expressions for the trajectory of influence under the uniform threshold distribution. Also, for an exponentially distributed threshold, we show that the fluid dynamics are equivalent to the well-known SIR model in epidemiology. We also numerically study how other hazard functions (obtained from the Weibull and loglogistic distributions) provide qualitative different characteristics of the influence evolution, compared to traditional epidemic models, even in a homogeneous setting. We finally show how the model can be extended to a setting with multiple communities and conclude with possible future directions.

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arXiv2014

The Nobel Prize delay

The time lag between the publication of a Nobel discovery and the conferment of the prize has been rapidly increasing for all disciplines, especially for Physics. Does this mean that fundamental science is running out of groundbreaking discoveries?

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arXiv2014

Insights from the Wikipedia Contest (IEEE Contest for Data Mining 2011)

The Wikimedia Foundation has recently observed that newly joining editors on Wikipedia are increasingly failing to integrate into the Wikipedia editors' community, i.e. the community is becoming increasingly harder to penetrate. To sustain healthy growth of the community, the Wikimedia Foundation aims to quantitatively understand the factors that determine the editing behavior, and explain why most new editors become inactive soon after joining. As a step towards this broader goal, the Wikimedia foundation sponsored the ICDM (IEEE International Conference for Data Mining) contest for the year 2011. The objective for the participants was to develop models to predict the number of edits that an editor will make in future five months based on the editing history of the editor. Here we describe the approach we followed for developing predictive models towards this goal, the results that we obtained and the modeling insights that we gained from this exercise. In addition, towards the broader goal of Wikimedia Foundation, we also summarize the factors that emerged during our model building exercise as powerful predictors of future editing activity.

cs.CY↗

arXiv2014

An improved vulnerability index of complex networks based on fractal dimension

With an increasing emphasis on network security, much more attention has been attracted to the vulnerability of complex networks. The multi-scale evaluation of vulnerability is widely used since it makes use of combined powers of the links' betweenness and has an effective evaluation to vulnerability. However, how to determine the coefficient in existing multi-scale evaluation model to measure the vulnerability of different networks is still an open issue. In this paper, an improved model based on the fractal dimension of complex networks is proposed to obtain a more reasonable evaluation of vulnerability with more physical significance. Not only the structure and basic physical properties of networks is characterized, but also the covering ability of networks, which is related to the vulnerability of the network, is taken into consideration in our proposed method. The numerical examples and real applications are used to illustrate the efficiency of our proposed method.

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