Lorenzo Coviello

Detecting emotional contagion in massive social networks

Happiness and other emotions spread between people in direct contact, but it is unclear whether massive online social networks also contribute to this spread. Here, we elaborate a novel method for measuring the contagion of emotional expression. With data from millions of Facebook users, we show that rainfall directly influences the emotional content of their status messages, and it also affects the status messages of friends in other cities who are not experiencing rainfall. For every one person affected directly, rainfall alters the emotional expression of about one to two other people, suggesting that online social networks may magnify the intensity of global emotional synchrony.

Stabilization Over Markov Feedback Channels: The General Case

The problem of mean-square stabilization of a discrete-time linear dynamical system over a Markov time-varying digital feedback channel is studied. In the scalar case, it is shown that the system can be stabilized if and only if a Markov jump linear system describing the evolution of the estimation error at the decoder is stable -videlicet if and only if the product of the unstable mode of the system and the spectral radius of a second moment matrix that depends only on the Markov feedback rate is less than one. This result generalizes several previous data rate theorems that appeared in the literature, quantifying the amount of instability that can be tolerated when the estimated state is received by the controller over a noise-free digital channel. In the vector case, a necessary condition for stabilizability is derived and a corresponding control scheme is presented, which is tight in some special cases and which strictly improves on a previous result on stability over Markov erasure channels.

Stabilization over Markov feedback channels

The problem of mean square stabilization of a discrete-time linear dynamical system over a Markov time-varying digital feedback channel is studied. In the scalar case, it is shown that the system can be stabilized if and only if a Markov jump linear system describing the evolution of the estimation error at the decoder is stable — videlicet if and only if the product of the unstable mode of the system and the spectral radius of a matrix that depends only on the Markov feedback rate is less than one. This result generalizes several previous data rate theorems that appeared in the literature, quantifying the amount of instability that can be tolerated when the estimated state is received by the controller over a noise free digital channel. In the vector case, a necessary condition for stabilizability is derived and a corresponding scheme is presented, which is tight in some special cases and which improves upon previous results on stability over Markov erasure channels.

Distributed team formation in multi-agent systems: Stability and approximation

We consider a scenario in which leaders are required to recruit teams of followers. Each leader cannot recruit all followers, but interaction is constrained according to a bipartite network. The objective for each leader is to reach a state of local stability in which it controls a team whose size is equal to a given constraint. We focus on distributed strategies, in which agents have only local information of the network topology and propose a distributed algorithm in which leaders and followers act according to simple local rules. The performance of the algorithm is analyzed with respect to the convergence to a stable solution. Our results are as follows. For any network, the proposed algorithm is shown to converge to an approximate stable solution in polynomial time, namely the leaders quickly form teams in which the total number of additional followers required to satisfy all team size constraints is an arbitrarily small fraction of the entire population. In contrast, for general graphs there can be an exponential time gap between convergence to an approximate solution and to a stable solution.

Human matching behavior in social networks: an algorithmic perspective.

We argue that algorithmic modeling is a powerful approach to understanding the collective dynamics of human behavior. We consider the task of pairing up individuals connected over a network, according to the following model: each individual is able to propose to match with and accept a proposal from a neighbor in the network; if a matched individual proposes to another neighbor or accepts another proposal, the current match will be broken; individuals can only observe whether their neighbors are currently matched but have no knowledge of the network topology or the status of other individuals; and all individuals have the common goal of maximizing the total number of matches. By examining the experimental data, we identify a behavioral principle called prudence, develop an algorithmic model, analyze its properties mathematically and by simulations, and validate the model with human subject experiments for various network sizes and topologies. Our results include i) a -approximate maximum matching is obtained in logarithmic time in the network size for bounded degree networks; ii) for any constant , a -approximate maximum matching is obtained in polynomial time, while obtaining a maximum matching can require an exponential time; and iii) convergence to a maximum matching is slower on preferential attachment networks than on small-world networks. These results allow us to predict that while humans can find a “good quality” matching quickly, they may be unable to find a maximum matching in feasible time. We show that the human subjects largely abide by prudence, and their collective behavior is closely tracked by the above predictions.

Words on the Web: Noninvasive Detection of Emotional Contagion in Online Social Networks

Does semantic expression spread online from person to person? And if so, what kinds of expression are most likely to spread? To address these questions, we developed a nonexperimental, noninvasive method to detect and quantify contagion of semantic expression in massive online social networks, which we review and discuss here. Using only observational data, the method avoids performing emotional experiments on users of online social networks, a research practice that recently became an object of criticism and concern. Our model combines geographic aggregation and instrumental variables regression to measure the effect of an exogenous variable on an individuals expression and the influence of this change on the expression of others to whom that individual is socially connected. In a previous work, we applied our method to the emotional content of posts generated by a large sample of users over a period of three years. Those results suggest that each post expressing a positive or negative emotion can cause friends to generate one to two additional posts expressing the same emotion, and it also inhibits their use of the opposite emotion. Here, we generalize our method so it can be applied to contexts different than emotional expression and to different forms of content generated by the users of online platforms. The method allows us to determine the usage of words in the same semantic category spread, and to estimate a signed relationship between different semantic categories, showing that an increase in the usage of one category alters the usage of another category in ones social contacts. Finally, it also allows us to estimate the total cumulative effect that a person has on all of her social contacts.

Weather impacts expressed sentiment

We conduct the largest ever investigation into the relationship between meteorological conditions and the sentiment of human expressions. To do this, we employ over three and a half billion social media posts from tens of millions of individuals from both Facebook and Twitter between 2009 and 2016. We find that cold temperatures, hot temperatures, precipitation, narrower daily temperature ranges, humidity, and cloud cover are all associated with worsened expressions of sentiment, even when excluding weather-related posts. We compare the magnitude of our estimates with the effect sizes associated with notable historical events occurring within our data.

Predicting and containing epidemic risk using friendship networks

Physical encounter is the most common vehicle for the spread of infectious diseases, but detailed information about said encounters is often unavailable because expensive, unpractical to collect or privacy sensitive. The present work asks whether the friendship ties between the individuals in a social network can be used to successfully predict and contain epidemic risk. Using a dataset from a popular online review service, we build a time-varying network that is a proxy of physical encounter between users and a static network based on their reported friendship — the encounter network and the friendship network. Through computer simulation, we compare infection processes on the resulting networks and show that friendship provides a poor identification of the individuals at risk if the infection is driven by physical encounter. This result is not driven by the static nature of the friendship network opposed to the time-varying nature of the encounter network, as a static version of the encounter network provides more accurate prediction of risk than the friendship network. Despite this limit, the information enclosed in the friendship network can be leveraged for monitoring and containment of epidemics. In particular, we show that periodical and relatively infrequent monitoring of the infection on the encounter network allows to correct the predicted infection on the friendship network and to achieve satisfactory prediction accuracy. In addition, the friendship network contains valuable information to effectively contain epidemic outbreaks when a limited budget is available for immunization.