the influence of social norms on synchronous versus...
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The Influence of Social Norms on Synchronous versusAsynchronous Communication Technologies
Abdullah AlmaatouqCenter for Complex
Engineering Systems atKACST and MIT
Fahad AlhasounCenter for Complex
Engineering Systems atKACST and [email protected]
Riccardo CampariMassachusetts Institute of
Anas AlfarisCenter for Complex
Engineering Systems atKACST and [email protected]
ABSTRACTExtensive theoretic work attempts to address the role ofsocial norms in describing, explaining and predicting hu-man behaviors. However, traditional methods of assessingthe effect can be expensive and time consuming. In thiswork, we utilize data generated by the call detail records(CDRs) and geo-tagged Tweets (GTTs) as enabling prox-ies for understanding human activity patterns. We presentpreliminary results on the effect of social norms on communi-cation patterns during different times of the day, includingprayer times. Specifically, we investigate the variations inpopulation behavioral patterns with respect to social normsbetween asynchronous (i.e., Twitter) and synchronous (i.e.,phone calls) communication mediums in the city of Riyadh,the capital of Saudi Arabia.
Categories and Subject DescriptorsH.4 [Information Systems Applications]: Communica-tions Applications, Miscellaneous
KeywordsSocial Norms, Social Media, Activity patterns, Call DetailRecords
1. INTRODUCTIONWith the rapid adoption of technologies, a significant por-
tion of the world’s population utilizes mobile phones, emailsand social media (e.g., Twitter, Facebook) as mediums formuch of their communication. These communication tech-nologies have formed a platform for people to exchange infor-
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mation, broadcast thoughts and convey feelings. Although,mobile phones and Twitter can be used for both unilateral(one-way) and bilateral (two-ways) communications, mobilephone calls are mostly synchronous bilateral communicationmedium [7] and Twitter is typically used as a unilateral asyn-chronous broadcasting platform [14].
While social norms can impact the way these technologiesare used, traditional methods of assessing the effect can beexpensive and time consuming. Such assessments are usu-ally in the form of surveys or lab experiments with consid-erably small sample sizes compared to the total population.Furthermore, such methods lack the accuracy and resolutionin time or space to provide fine-grained analysis of humanactivity. Hence, we utilize mobile phone data, and onlineuser-generated social media (see Section 3) in the urban ar-eas of Saudi Arabia to improve our understanding of theinfluence of social norms in probing the communication pat-terns during the different times of the day (see Section 4.1),and during prayer times (see Section 4.2).
2. BACKGROUNDThe Kingdom of Saudi Arabia (KSA) covers most of the
Arabian Peninsula, with an area of about 2 million km2
and approximately 28 million inhabitants [4]. Over the pastdecade, the Kingdom has taken strong steps towards devel-oping a diversified economy. Specifically on enhancing itsInformation and Communication Technology (ICT) infras-tructure. Today, Saudi Arabia has one of the highest inter-net penetration percentages in the gulf area [5] with currentpenetration at 14.7 million. It is ranked among the highestcountries worldwide in mobile penetration rates with 188%of the population possess mobile phones [5]. Furthermore,the Kingdom is currently ranked second among the world’sfastest growing countries on Twitter, and Riyadh (the cap-ital of KSA) held the 10th position in terms of the totalnumber of tweets per month [13]. Similarly, the countryis undergoing an industrial growth in multiple sectors withmany services going online.
KSA is an Islamic country, governed by the Islamic laws,and the home of the two Holy Mosques for Muslims. In Is-lam, there are five daily prayers that a Muslim follower must
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Figure 1: Mobile (left) and Twitter (right) time-cumulative spatial activity density in Riyadh
perform during different times of the day [3]. The five timesare Fajr (dawn), Duhr (midday), Asr (afternoon), Maghrib(sunset) and Isha (night). The prayer times (start time andend time) for each of the prayers is based on the angle ofthe sun. Therefore, different geographical locations withinthe same country may have different prayer times [11].
KSA represents a unique and convergent blend of socialconservatism [12]. The Islamic religion plays an importantrole in defining the cultural norms, social practices and tra-ditional obligations in Saudi Arabia [1, 10]. The culturalsensitivity in Saudi Arabia has influenced the process ofdeveloping systems and adopting policies (e.g., halting allcommercial operations and activities during prayer time).
3. DATASETSThe large amount of spatio-temporal data contained in the
Information and Communication Technologies (ICT) infras-tructures presents an opportunity for the study of humanbehavior on a larger scale [15, 9, 6]. For instance, Call DetailRecords (CDRs) are one type of information Telecom com-panies keep for billing purposes. Every time a user makes aphone call, sends a text message, or uses the Internet, themobile network keeps a record of their usage informationand location.
Another useful source for spatio-temporal data is Geo-Tagged Tweets (GTT) [2, 16]. According to the Global WebIndex report [8], Twitter is the fastest growing social plat-form in the world. In addition to the temporal informationincluded in the GTT (i.e., content creation time stamp),it also can provide rich socioeconomic context informationregarding the social demographic characteristics of the in-dividuals (e.g., gender, language, sentiment), in addition tothe following spatial information: (i) Exact Location (Lati-tude/Longitude coordinate) that describe the exact locationof a Tweet; (ii) Twitter Place, which can be a point of in-terest provided by the user with varying granularity (e.g.,Neighborhood, City, Admin Boundary, or Country).
In this work, we utilize approximately 100 million CDRsand 26 thousands GTT as the enabling proxies for under-
standing the patterns of human activity, in particular theinfluence of social norms on communication patterns.
4. PRELIMINARY ANALYSISIn this paper, we investigate the variations within popula-
tion behavioral characteristics with respect to social normsin the Kingdom of Saudi Arabia. Specifically, we com-pare the influence of social norms on synchronous and asyn-chronous communication mediums in the city of Riyadh, thecapital of Saudi Arabia. Section 4.1 presents a preliminaryevidence of the influences social norms place on the patternsof communicating at different times of the day. Section 4.2provides insights on how religious norms could influence thecommunication patterns.
4.1 Time of Day InfluenceWe first investigate the existence of varying usage pat-
terns between asynchronous mediums (i.e., Twitter) andsynchronous mediums (i.e., phone calls) based on the tem-poral dimension (time of the day). Fig 2 shows the distri-bution of phone call activity versus Twitter activity as afunction of time. The dashed vertical lines represent tip-ping points around the day where the majority of usage as apercentage of the population flips between Twitter activityto phone calls and vice versa. In Fig 2, we observe vary-ing peak times of activity between phone calls and Twitter;phone call activity peaks at around 5pm, while Twitter ac-tivity peaks close to midnight. The figure shows that peopletend to make more phone calls during day times and earlynight-time. In addition, we see a sharp decreasing patternof the mobile phone call activity from 9pm onward indicat-ing a decrease in the tendency of making phone calls duringlate night times. On the other hand, the majority of Twit-ter communication patterns occurs during night-time andspecifically around midnight.
The observed discrepancy of usage patterns could be ex-plained by the social norms governing the society. Peopletend to engage in synchronous communication at times whenit is socially acceptable to do so. This is the case of phone
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call activities where the majority of phone calls occur be-tween 7am and 9pm. On the other hand, such social bur-den on communication activity is not evident in the caseof Twitter activity levels. In Twitter, the communicationis of asynchronous nature, where the recipients are not ob-ligated to reply in order for the communicating process tocomplete. This could explain the pick up in Twitter activ-ity during the night-time where activity levels are negativelycorrelated with phone call activity. This also indicates thatthe unilateral asynchronous nature of Twitter is not subjectto the constraint of socially acceptable times of communica-tion.
2 4 6 8 10 12 14 16 18 20 22 24
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0.04
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Den
sity
GTTPhone Calls
Figure 2: Mobile and Twitter temporal activity den-sity in Riyadh
Business hours could also have an effect on the commu-nication activities. For instance, the majority of phone callactivities appeared to coincide with the daily working hours.To further investigate such pattern, the distribution of mo-bile phone activity is decomposed onto the spatial dimen-sion shown in Fig 1. The decomposition of calls onto spaceshows high activity levels of mobile phone calls at the centerof Riyadh, where most businesses are located. On the otherhand, Twitter spatial activity levels having its peak late atnight shows higher density at the residential areas. This cor-responds to time series activity patterns during night timeswhere most people are expected to be at home.
4.2 Prayers InfluenceThe pattern of mobile phone activity around the day dis-
cussed in section 4.1 shows significant drops occurring fourtimes across the day. Further investigation shows that thosedrops coincide with daily prayer times. In order to validatethe correlation between the drops in activity with prayertimes, the analysis was expanded to include cities with dif-ferent prayer times. Figure 4 illustrates activity levels ofthree major cities in Saudi Arabia from east to west, namelyDammam, Riyadh and Jeddah. Drop in activity for a prayertime appeared to propagate across the country from east towest as prayer times in the west are later than those in theeast. In addition, the lag in time between the city of Riyadhin the central region and Dammam on the east coast is less
than that between Riyadh and Jeddah on the west coast,this is due to the fact that the city of Riyadh is on a lon-gitude line that is closer to that of Dammam than that ofJeddah.
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Time (Hours)
Den
sity
Riyadh
AsrPrayer
MagribPrayer
IshaPrayer
DuhrPrayer
FajrPrayer
Figure 3: Twitter Activity in Riyadh
Observing the drops of phone calls activity indicate a so-cial phenomenon that is driven by prayers. To investigateif such phenomenon withholds in asynchronous communica-tion schemes, we observe activity levels of Twitter acrossprayer times of the city of Riyadh. Fig 3 shows that dropsin Twitter activity during prayer times are less evident thanthat of mobile phone activity. The variation in the magni-tude of drops in activity between Twitter and mobile phonecalls could be explained by the social norms of the commu-nity, where people tend to consider the other party beforemaking a phone call. Another potential evidence of suchhypothesis is that the phone calls activity level tends to in-crease rapidly after prayer times to a higher level than thatprior to prayer times. This hints that people would considerdelaying phone calls until prayer time ends. The observedpatterns in activities during prayer times indicate a vari-ation in behavior between synchronous and asynchronouscommunications resulting from the norms of the society.
5. FUTURE WORKFurther research will study the effect of social norms on
human mobility within the city of Riyadh by utilizing thespatial dimension of the data. Existing methods of analyzinghuman mobility will be incorporated to investigate whethermobility patterns during prayer times vary from the norm.One way of approaching the analysis of human mobility pat-terns during prayer times is to analyze the distributions oftrips characteristics (i.e. length, origins,destinations...etc)during prayer times. Another approach might consider thecorrelation between the number of trips across the day andthe time remaining to the next prayer, to investigate whetherprayer times have wider influence on mobility that spansnon-prayer times.
6. CONCLUSIONSThis paper presented a preliminary analysis of the influ-
ence cultural norms place on communication patterns in the
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2 4 6 8 10 12 14 16 18 20 22 24
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Den
sity
Dammamj(Eastern)Riyadhj(Central)Jeddahj(Western)
FajrPrayer
DuhrPrayer
AsrPrayer
MagribPrayer
IshaPrayer
Figure 4: Mobile phone call activity in Dammam,Riyadh and Jeddah
city of Riyadh, Saudi Arabia. The paper illustrated anal-ysis of the activities with respect to time and communica-tion mediums. Although the analysis demonstrated discrep-ancies in behavioral patterns between Twitter and mobilephone call records, further research is required to quantifythe influence of social factors on communication patterns.
7. ACKNOWLEDGMENTSThe authors would like to thank King Abdulaziz City for
Science and Technology (KACST) for funding this work. Inaddition, the authors thank the Center for Complex En-gineering Systems (CCES) at KACST and MIT for theirsupport.
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