14 research outputs found
Quantifying Information Flow During Emergencies
Recent advances on human dynamics have focused on the normal patterns of human activities, with the quantitative understanding of human behavior under extreme events remaining a crucial missing chapter. This has a wide array of potential applications, ranging from emergency response and detection to traffic control and management. Previous studies have shown that human communications are both temporally and spatially localized following the onset of emergencies, indicating that social propagation is a primary means to propagate situational awareness. We study real anomalous events using country-wide mobile phone data, finding that information flow during emergencies is dominated by repeated communications. We further demonstrate that the observed communication patterns cannot be explained by inherent reciprocity in social networks, and are universal across different demographics
Information spreading during emergencies and anomalous events
The most critical time for information to spread is in the aftermath of a
serious emergency, crisis, or disaster. Individuals affected by such situations
can now turn to an array of communication channels, from mobile phone calls and
text messages to social media posts, when alerting social ties. These channels
drastically improve the speed of information in a time-sensitive event, and
provide extant records of human dynamics during and afterward the event.
Retrospective analysis of such anomalous events provides researchers with a
class of "found experiments" that may be used to better understand social
spreading. In this chapter, we study information spreading due to a number of
emergency events, including the Boston Marathon Bombing and a plane crash at a
western European airport. We also contrast the different information which may
be gleaned by social media data compared with mobile phone data and we estimate
the rate of anomalous events in a mobile phone dataset using a proposed anomaly
detection method.Comment: 19 pages, 11 figure
Characterizing information leaders in Twitter during COVID-19 crisis
Information is key during a crisis such as the current COVID-19 pandemic as
it greatly shapes people opinion, behaviour and even their psychological state.
It has been acknowledged from the Secretary-General of the United Nations that
the infodemic of misinformation is an important secondary crisis produced by
the pandemic. Infodemics can amplify the real negative consequences of the
pandemic in different dimensions: social, economic and even sanitary. For
instance, infodemics can lead to hatred between population groups that fragment
the society influencing its response or result in negative habits that help the
pandemic propagate. On the contrary, reliable and trustful information along
with messages of hope and solidarity can be used to control the pandemic, build
safety nets and help promote resilience and antifragility. We propose a
framework to characterize leaders in Twitter based on the analysis of the
social graph derived from the activity in this social network. Centrality
metrics are used to identify relevant nodes that are further characterized in
terms of users parameters managed by Twitter. We then assess the resulting
topology of clusters of leaders. Although this tool may be used for
surveillance of individuals, we propose it as the basis for a constructive
application to empower users with a positive influence in the collective
behaviour of the network and the propagation of information
A Linguistically-driven Approach to Cross-Event Damage Assessment of Natural Disasters from Social Media Messages
This work focuses on the analysis of Italian social media messages for disaster management and aims at the detection of messages carrying critical information for the damage assessment task. A main novelty of this study consists in the focus on out-domain and cross-event damage detection, and on the investigation of the most relevant tweet-derived features for these tasks. We devised different experiments by resorting to a wide set of linguistic features qualifying the lexical and grammatical structure of a text as well as ad-hoc features specifically implemented for this task. We investigated the most effective features that allow to achieve the best results. A further result of this study is the construction of the first manually annotated Italian corpus of social media messages for damage assessment
Which friends are more popular than you? Contact strength and the friendship paradox in social networks
The friendship paradox states that in a social network, egos tend to have lower degree than their alters, or, “your friends have more friends than you do”. Most research has focused on the friendship paradox and its implications for information transmission, but treating the network as static and unweighted. Yet, people can dedicate only a finite fraction of their attention budget to each social interaction: a high-degree individual may have less time to dedicate to individual social links, forcing them to modulate the quantities of contact made to their different social ties. Here we study the friendship paradox in the context of differing contact volumes between egos and alters, finding a connection between contact volume and the strength of the friendship paradox. The most frequently contacted alters exhibit a less pronounced friendship paradox compared with the ego, whereas less-frequently contacted alters are more likely to be high degree and give rise to the paradox. We argue therefore for a more nuanced version of the friendship paradox: “your closest friends have slightly more friends than you do”, and in certain networks even: “your best friend has no more friends than you do”. We demonstrate that this relationship is robust, holding in both a social media and a mobile phone dataset. These results have implications for information transfer and influence in social networks, which we explore using a simple dynamical model.James P. Bagrow, Christopher M. Danforth and Lewis Mitchel