Quantifying gender preferences in human social interactions using a large cellphone dataset

Ghosh, Asim; Monsivais, Daniel; Bhattacharya, Kunal; Dunbar, Robin; Kaski, Kimmo

doi:10.1140/epjds/s13688-019-0185-9

Cited by 16 publications

(16 citation statements)

References 35 publications

(42 reference statements)

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“…Aside from this, there may also be differences between parent-child pairs that co-reside and those that do not. Further investigation into the location data as well as egocentric communication patterns in this group may shed light on this issue [28,29].…”

Section: Discussionmentioning

confidence: 98%

Link-centric analysis of variation by demographics in mobile phone communication patterns

et al. 2020

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We present a link-centric approach to study variation in the mobile phone communication patterns of users. Unlike most previous research on call detail records that focused on the variation of phone usage across individual users, we examine how the calling and texting patterns obtained from call detail records vary among pairs of users and how these patterns are affected by the nature of relationships between users. To demonstrate this link-centric perspective, we extract factors that contribute to the variation in the mobile phone communication patterns and predict demographics-related quantities for pairs of users. The time of day and the channel of communication (calls or texts) are found to explain most of the variance among pairs that frequently call each other. Furthermore, we find that this variation can be used to predict the relationship between the pairs of users, as inferred from their age and gender, as well as the age of the younger user in a pair. By looking at the classifier performance across different age and gender groups, we gain insights into how communication patterns vary across different relationships.August 1, 2019 1/19 networks [23,24] have also been adopted. These studies have mainly relied on the egocentric perspective, working on the implicit assumption that an individual's traits determine his or her calling and texting patterns. Thus, when using an egocentric approach in predicting individual user information, all calls and texts made by the target individual are aggregated over all its neighbors, so the links in the target individual's egocentric network are not differentiated from each other. While the communication patterns have been shown to vary with age and gender at the individual level as discussed above, the communication patterns between two individuals have also been found to vary with the relationship between them [25,26]. However, while an egocentric perspective works well in studying variation at the individual level, it cannot, by design, provide much insight into variation among pairs. We hope to expand knowledge in this area by focusing on the links rather than the nodes in the mobile phone communication network, analyzing each link independently from others instead of aggregating them over the egocentric network. Such link-centric approach has not been explored much in relation to mobile phone data [27,28], especially in prediction tasks. In particular, only Herrera et al. [23] have used isolated link information as well as the demographic information of one user in the link to predict the age and gender of the other user. Still, as their aim was to predict individual user information, they did not touch on the connection between the communication patterns and the relationships between the pairs of users.It is important to note that while the age and gender can be categorically confirmed from the datasets, determining the relationships between any pair of users from the mobile phone data relies on inference. In particular, unless otherwise specified, we mean such in...

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Section: Discussionmentioning

confidence: 98%

Link-centric analysis of variation by demographics in mobile phone communication patterns

et al. 2020

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“…This trait may be a direct cause of societal responsibilities that are usually assumed to be taken predominantly by women e.g. child care etc [44].…”

Section: Discussionmentioning

confidence: 99%

Morningness-eveningness assessment from mobile phone communication analysis

Roy

Monsivais

Bhattacharya

et al. 2021

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Human behaviour follows a 24-hour rhythm and is known to be governed by the individual chronotypes. Due to the widespread use of technology in our daily lives, it is possible to record the activities of individuals through their different digital traces. In the present study we utilise a large mobile phone communication dataset containing time stamps of calls and text messages to study the circadian rhythms of anonymous users in a European country. After removing the effect of the synchronization of East-West sun progression with the calling activity, we used two closely related approaches to heuristically compute the chronotypes of the individuals in the dataset, to identify them as morning persons or ``larks'' and evening persons or ``owls''. Using the computed chronotyes we showed how the chronotype is largely dependent on age with younger cohorts being more likely to be owls than older cohorts. Moreover, our analysis showed how on average females have distinctly different chronotypes from males. Younger females are more larkish than males while older females are more owlish. Finally, we also studied the period of low calling activity for each of the users which is considered as a marker of their sleep period during the night. We found that while ``extreme larks'' tend to sleep more than ``extreme owls'' on the weekends, we do not observe much variation between them on weekdays. In addition, we have observed that women tend to sleep even less than males on weekdays while there is not much difference between them on the weekends.

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“…In this paper, we use the Call Data Records (CDR) to understand segregation in Estonian society. Over the last decade, CDR has been analysed from a number of perspectives, such as social network analysis [4], sociocultural aspects of a city [5], identifying the human mobility patterns [6], understanding calling patterns using phone call duration [7], impact of various events on calling activities [8] and population distribution [9] to name a few. lso, CDR often integrates with other data such as traffic data [10], financial data [11], and GIS data [12] for a deeper understanding of human behavior.…”

Section: Introductionmentioning

confidence: 99%

“…lso, CDR often integrates with other data such as traffic data [10], financial data [11], and GIS data [12] for a deeper understanding of human behavior. Previous studies have also used CDR data to explain the ethnic discrimination in society using call duration [7,13], and social group discrimination at the workplace [14]. This work investigates gender segregation within society by analyzing the users' characteristics and their interaction through social network analysis.…”

Section: Introductionmentioning

confidence: 99%

Understanding gender segregation through Call Data Records: An Estonian case study

2021

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Understanding segregation plays a significant role in determining the development pathways of a country as it can help governmental and other concerned agencies to prepare better-targeted policies for the needed groups. However, inferring segregation through alternative data, apart from governmental surveys remains limited due to the non-availability of representative datasets. In this work, we utilize Call Data Records (CDR) provided by one of Estonia’s major telecom operators to research the complexities of social interaction and human behavior in order to explain gender segregation. We analyze the CDR with two objectives. First, we study gender segregation by exploring the social network interactions of the CDR. We find that the males are tightly linked which allows information to spread faster among males compared to females. Second, we perform the micro-analysis using various users’ characteristics such as age, language, and location. Our findings show that the prime working-age population (i.e., (24,54] years) is more segregated than others. We also find that the Estonian-speaking population (both males and females) are more likely to interact with other Estonian-speaking individuals of the same gender. Further to ensure the quality of this dataset, we compare the CDR data features with publicly available Estonian census datasets. We observe that the CDR dataset is indeed a good representative of the Estonian population, which indicates that the findings of this study reasonably reflect the reality of gender segregation in the Estonian Landscape.

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Quantifying gender preferences in human social interactions using a large cellphone dataset

Cited by 16 publications

References 35 publications

Link-centric analysis of variation by demographics in mobile phone communication patterns

Link-centric analysis of variation by demographics in mobile phone communication patterns

Morningness-eveningness assessment from mobile phone communication analysis

Understanding gender segregation through Call Data Records: An Estonian case study

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