Citekey: @Onnela2014

Onnela, J.-P., Waber, B. N., Pentland, A., Schnorf, S., & Lazer, D. (2014). Using sociometers to quantify social interaction patterns. Scientific Reports, 4, 1–8. doi:10.1038/srep05604


One of the most clear description of how sociometers work. Study focuses on gender difference in terms of proximity and talkativeness. “The strongest effect discovered in our study is the difference in gender participation as a function of tie strength and group size.”


self-reported accounts of behaviour are prone to biases and necessarily reduce the range of behaviours, and the number of subjects, that may be studied simultaneously. (p. 1)

The development of ever smaller sensors makes it possible to study group-level human behaviour in naturalistic settings outside research laboratories. We used such sensors, sociometers, to examine gender, talkativeness and interaction style in two different contexts. Here, we find that in the collaborative context, women were much more likely to be physically proximate to other women and were also significantly more talkative than men, especially in small groups. In contrast, there were no gender-based differences in the non-collaborative setting. (p. 1)

esearch on human social interactions has relied on observations reported by humans, and both selfreported data and observer-recorded data, with varying degrees of observer involvement, have been used to quantify interactions. (p. 1)

Analysing human behaviour based on electronically generated data has recently become popular. (p. 1)

Here, we used ‘‘sociometers,’’ which are wearable devices that use a high-frequency radio transmitter to gauge physical proximity to others, and a microphone to track speech, to collect detailed information on social interactions within particular contexts. (p. 1)

Early explorations with sociometers have shown that even short, sliced signals can be powerful predictors for human communication4,8–10. We used the radio transmitter to infer whether and for how long any two participants were proximate to one another. The strength of the received signal was used to estimate the distance between sociometers, and we used a cut-off value of signal strength that corresponded to a physical distance of approximately 3 meters. The sociometer did not store raw audio data, but rather computed audio features that were used to infer speaking time, measured in seconds, for each participant. Finally, the signals from the built-in accelerometers, the third stream of data recorded, were used to ascertain that the participants wore the devices throughout the period of observation by monitoring the level of energy associated with their movement (see Methods). (p. 1)

Mobile phone communication data have recently enabled the exploration12 and modelling13 of (p. 1)

large-scale social networks, and they have also been used to investigate sex differences in the age and sex composition of conversation partners14. (p. 2)

To carry out the analyses, in each Setting we first divided the 12 hours of data into 144 segments, corresponding to 5-minute time windows. In the resulting networks constructed from these data segments, any two individuals were linked if they had been proximate to one another for at least the duration of one full time window. Encounters that did not fully cover at least one time window were deemed inconsequential and were not included as ties in the network. (p. 2)

Results (p. 2)

To explore the relationship between gender and context, we collected data in two settings (p. 2)

Setting 1 encompassed the first day (12 hours) of an intense one-week long collaborative exercise at the end of the first year of a two-year Master’s program at a public policy school in a private US university. The students, who had previously earned their first degrees and were now pursuing subsequent professional degrees, were required to process a large quantity of sophisticated readings and lectures, within a week, into a memo with a policy recommendation. Communication with other students was allowed. The performance of the students in the exercise affected their final course grade. We collected and analysed data from 79 students (42 males, 37 females). Setting 2, in contrast to Setting 1, was entirely unstructured and non-collaborative. We collected data from 54 co-located employees (16 males and 38 females) at a call centre in a major US banking firm. We analysed their behaviour during 12 one-hour lunch breaks, spanning several weeks, which the employees would typically spend in a cafeteria, in relatively small groups, in the same building. (p. 2)

Proximity (p. 2)

Using 5minute windows, the mean degree of subjects was 4.5 in Setting 1 and (p. 2)

7.1 in Setting 2. In both settings, females and males had a similar number of connections, 4.8 vs. 4.1 in Setting 1 and 7.1 vs. 7.2 in Setting 2, respectively. (p. 3)

To incorporate the role of tie persistence in our analyses, we used the measured durations of proximity as tie strengths in the resulting aggregate network. (p. 3)

Talkativeness (p. 4)

The talkativeness of individuals is computed in a large number of time windows; although the raw data are collected at 750 Hz, the audio features are calculated at 50 Hz, still a high frequency. Instead of dealing with the raw audio signal, we use the variance of the audio signal in a range of frequencies typically associated with human voice (see Methods and Fig. 5), which is a more robust way to distinguish between whether the signal comes from the person wearing the sociometer, or whether it corresponds to ambient noise (e.g., someone else talking). (p. 4)

We quantify the talkativeness of a person by taking the average of the variance of voice signal in each window (p. 4)

The data from each setting can be represented as matrix X, where the rows corresponds to the subjects (i) and the columns to the time windows (t). We express the average talkativeness of a person, male or female, by the temporal average yi ~ð1=T Þ XT xit . To compare t~1 the talkativeness of males and females, we compute the median of the y variables for males and females, resulting in ~y and ~y , respectiMF ively. (p. 4)

The distribution of the talk-ratio variable r under the gender-neutral null model is shown in Fig. 2 (see also Methods). We found that the observed value of 1.62 in Setting 1 is statistically significantly different from 1 (p , 0.01), whereas the value of 1.04 in Setting 2 is not. We conclude that women are substantially more talkative in Setting 1, but there is no difference in the talkativeness of men and women in Setting 2. (p. 4)

While weak (short aggregate proximity) ties may be ‘‘accidental,’’ strong ties (extensive aggregate proximity) more likely are evidence of intended social interactions. We found that in Setting 1, there is a systematic overrepresentation of FF ties and an under-representation of MM ties. Further, this over-representation of FF ties increases monotonically with the threshold weight w as the following results, based on 10,000 permutation replications, demonstrate. In the non-thresholded network (threshold w 5 0), there is only weak evidence to suggest that FF-ties might be over-represented: We obtain the ratio 1.21 (90% CI: 0.96, 1.52) under unconstrained permutation and 1.01 (90% CI: 0.92, 1.08) under constrained permutation. However, in the strongly thresholded network (threshold w 5 20), FF-ties are substantially overrepresented: We obtain the ratio 2.94 (90% CI: 1.20, 6.00) under unconstrained permutation and 1.98 (90% CI: 1.04, 3.43) under constrained permutation. In contrast to Setting 1, in Setting 2 there is no perceptible statistically significant relationship between the frequency of FF, FM, or MM ties. (p. 4)

We combine the proximity and talkativeness data of sociometers to examine talkativeness as a function of interaction partners. (p. 4)

Taken together, in Setting 1 women appear to have more high-persistence ties than men do, whereas in Setting 2 this is not the case. (p. 4)

Discussion (p. 5)

The strongest effect discovered in our study is the difference in gender participation as a function of tie strength and group size. (p. 5)

Specifically, an earlier study, which did not consider the proximity of others, found no significant gender differences in individual talkativeness15, compatible with our results for Setting 2. In Setting 1, women were much more likely to associate with other women than men, and thus were also more talkative (with the exception of the briefing). These findings are consistent with prior research indicating that women tend to have more interactive learning styles than men23. Our results also highlight the role of context. Constructionist and contextualist models of gender assert that activity is a highly influential moderator of gender-related variations in social behaviour7,24,25. Our results clearly support the notion (p. 5)

Figure 5 Using sociometers to study physical proximity and talkativeness. Top: Every participant in each Setting was wearing an identical sociometer, which had a built-in radio transmitter and microphone. The radio transmitter was able to sense others who were within a radius of approximately 3 meters from the subject. In the corresponding network representation, a tie connects two individuals if they have been within this distance for at least one time window. The builtin microphones were used to assess the talkativeness of each individual, giving rise to the metric x(t) for each individual as shown. Bottom: Schematic of the construction of the audio signals xi(t) and yi for node i. The raw audio signal, as recorded by the built-in microphone and temporarily stored on the sociometer for processing. This raw signal is first channeled to an array of band-pass filters encapsulating the range of typical human speech (see Methods), and the variance of the resulting human voice signal is shown. We then divide the temporal domain into time windows, and the average of the variance (lower panel) within each window gives the audio signal xi(t) in time window t, pictorially represented by the horizontal lines within each time window. To express the overall talkativeness of a person, we take the temporal average of xi(t) over all time windows, giving rise to yi for subject i. (p. 6)

gender differences in both physical proximity and talkativeness are strongly present in the more structured task-oriented context (Setting 1), whereas they completely disappear in unstructured non-task-oriented context (Setting 2). (p. 6)

Our results appear relevant also in a larger context. More and more problems are solved in groups, and recent studies have indeed shown that diverse groups of problem solvers, referring to groups of people with diverse tools and skills, consistently outperform groups of the best and the brightest, a finding captured by the aphorism ‘‘diversity trumps ability’’26,27. Research is also increasingly done in teams across nearly all fields, and teams typically produce more frequently cited research than individuals do, including the exceptionally highimpact research28. Another recent study suggests that a ‘‘general collective intelligence factor’’ of a group is not strongly correlated with the intelligence of the individual group members, but instead with the average social sensitivity of group members, the equality in distribution of conversational turn-taking, and the proportion of females in the group29. (p. 6)

Methods (p. 7)

Sociometer accelerometer. An accelerometer measures changes in experienced acceleration. The badge’s 3-axis accelerometer signal is sampled at 50 Hz, which is able to capture a wide range of human movement, given that 99% of the acceleration power during daily human activities is contained below 15 Hz30. The range of values for the accelerometer signal varies between 23 g and 13 g, where g 5 9.81 m/s2 is the gravitational acceleration. In our study, we used data from the accelerometer to determine if the participants wore the sociometers. Each accelerometer measured energy levels due to physical movement above the reference level of 1 unit4, ascertaining that the subjects wore the sociometers as instructed. (p. 8)

Sociometer microphone. The microphone within the badge did not store raw audio data, but rather computed audio features that were used to infer speaking patterns. The microphone in the sociometer was connected to an array of band-pass filters that divided the speech frequency spectrum into four octaves: (1) 85 to 222 Hz, (2) 222 to 583 Hz, (3) 583 to 1,527 Hz, and (4) 1,527 to 4,000 Hz. These frequencies encapsulate the range of typical human speech. In particular, in this study we examined the variation in the audio signal that arrived from sample to sample (the sampling frequency was 750 Hz). The more variation there is present in the signal, the more confident we can be that the associated signal is indeed human speech and not due to an external source. Audio features like these can be used not only to infer that a person is speaking, but they can also capture nonlinguistic social signals, such as interest and excitement31. (p. 8)

Sociometer radio. The built-in omni-directional 2.4 GHz radio was designed to detect physically proximate interactions. The radios sent a transmission once every minute that contained the ID of the sending badge, some synchronisation information, and error correction bytes. By measuring the received signal strength, it was possible to estimate the distance to the sender. We used a cut-off on the signal strength value to register people who were located within 3 meters of one another. This distance was deemed to be appropriate for detecting a level of physical proximity that likely corresponds to an intentional social interaction. Since the subjects were free to move around the premises, depending on the given physical environment surrounding them, which would affect the transmission of radio waves, there is an error of 1.5 meters on the distance estimates32. This means that we cannot rule out the possibility that two individuals at 4.5 meters would have appeared to be physically proximate and, similarly, it is possible that some individuals would have needed to be within 1.5 meters in order to have been registered as having been physically proximate. Due to these spurious detections, there are likely some false positive and some false negatives in our dataset. Identical instrumentation, i.e., the fact that each subject wore an identical device, ensures that there was no person-to-person variability in how distance (or any other behavioural signal) was measured.

  1. Hong, L. & Page, S. E. Groups of diverse problem solvers can outperform groups of highability problem solvers. Proc Natl Acad Sci USA 101, 16385–16389 (2004).

  2. Page, S. E. The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies (Princeton University Press) (2007).

  3. Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N. & Malone, T. W. Evidence for a collective intelligence factor in the performance of human groups. Science 330, 686–688 (2010).

Blog Logo

Bodong Chen



Crisscross Landscapes

Bodong Chen, University of Minnesota

Back to Home