Social networking in developing regions

Azarias RedaUniversity of Michigan

azarias@umich.edu

Sam ShahLinkedIn

samshah@linkedin.com

Mitul TiwariLinkedIn

mtiwari@linkedin.com

Anita LillieLinkedIn

alillie@linkedin.com

Brian NobleUniversity of Michiganbnoble@umich.edu

ABSTRACTOnline social networks have enjoyed signi�cant growth over the pastseveral years. With improvements in mobile and Internet penetra-tion, developing countries are participating in increasing numbersin online communities. �is paper provides the �rst large scale anddetailed analysis of social networking usage in developing countrycontexts. �e analysis is based on data from LinkedIn, a professionalsocial network with over million members worldwide. LinkedInhas members from every country in the world, including millionsin Africa, Asia, and South America. �e goal of this paper is to pro-vide researchers a detailed look at the growth, adoption, and othercharacteristics of social networking usage in developing countriescompared to the developed world. To this end, we discuss severalthemes that illustrate di�erent dimensions of social networking use,ranging from interconnectedness of members in geographic regionsto the impact of local languages on social network participation.

Categories and Subject DescriptorsH. [Information Systems]: Information Systems Applications;J. [Computer Applications]: Social and Behavioral Sciences

General TermsMeasurement, Human Factors

KeywordsDeveloping regions, social networks, emerging markets

1. INTRODUCTIONAs mobile and Internet penetration improve worldwide [], users

from developing countries are participating in increasing numbers inonline communities. Several studies of web usage patterns in devel-oping country contexts [, ] indicate Internet users in these areasare very engaged in online social networking and communicationtools, spending a signi�cant portion of their online time on them.�ese observations have been made across several usage scenarios,ranging from educational institutions in urban India to remote In-ternet access sites in Africa and Latin America. In addition, surveysand other anecdotal evidence has indicated that users from emerg-ing economies have been driving worldwide membership growth insocial networks [].

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�is paper provides the �rst large scale and detailed analysis ofsocial networking usage in developing country contexts. Our analysisis based on pro�le and activity data from LinkedIn, a professionalsocial networking site with, as of writing, over million membersworldwide. Over the past few years, online social networking has beenproviding a communication platform on a truly global scale unlikeanything the world has seen before. Its emphatic adoption by peoplefrom every corner of the world builds on many interesting patternsand characteristics that re�ect the underlying economic, social andcultural makeup of the participants. Using data from a commercialsocial networking site with a global membership base, this paperprovides an internal look at the social networking phenomena indeveloping country contexts, and how it compares with the rest ofthe world.

LinkedIn has members from every country in the world, includingseveral million in Africa. It also has a strong presence in Asia andLatin America, with countries like India and Brazil among the mostactive in the world. As a professional networking site, LinkedIn alsohas unique access to information such as career industries and educa-tional level of members. �is gives us a rich set of demographic andlocation data to work with, augmented by detailed activity informa-tion for the website. �is ranges from how members access the socialnetworking service to how they make connections and interact withother members. We combine pro�le information with activity datato analyze several aspects of social networking usage in developingcountries.Our analysis is presented in the form of several themes that il-

lustrate di�erent dimensions of social networking use. While wefocus on patterns and characteristics from developing countries, wewill also present contextual information from the rest of the world,which provides interesting comparisons emerging from the underly-ing di�erences and similarities in the member base. Some patternsare unique to the developing world, o�en shaped by economic, socialand cultural factors, or the brief history and attributes of Internetcitizenship for many users in these environments. Other patternstranscend geographic and economic barriers, and derive from basichuman social behavior in sharing, communication and interaction.�e goal of this paper is to provide researchers a revealing look onthe growth, adoption and characteristics of social networking in de-veloping countries. While we believe these characteristics are goodindicators of social networking use in emerging economies, it is im-portant to note that our analysis is solely based on data fromLinkedIn,only one of several commercial social networks.

�is paper discusses six characteristics and patterns, ranging fromthe interconnectedness of members in various geographic regionsto the demographic and educational makeup of participants. Socialnetworks enable members to make connections with other membersthroughout the world, and we will begin by investigating how peoplechoose to connect with each other. In particular, we will look at thegeographic locality of social network connections, and trends thatemerge from cross-country and cross-continental connections.

Figure : Region classi�cations

We then consider the overall engagement and activity of membersin developing countries, and how it compares with the rest of theworld. �is can be expressed in several terms, including the growth ofpersonal connections of members in the network, and the frequencyand length of visits compared to members from more connectedenvironments. �is is augmented by a look at access devices fromdeveloping regions. We will investigate how members are accessingsocial networking sites from various regions, and how this trendmapsthe increase in mobile and Internet penetration in many developingcountries.Another important component in understanding social network-

ing use is the demographic and educational makeup of members. Wewill explore generational bias in Internet access and participation, asdemonstrated in the age distribution of members across the world.In addition, we look at gender representation, and how cultural andaccess barriers are re�ected in social networking usage. Alongside de-mographics, we investigate the educational background and industryrepresentation of members from various developing countries andcorresponding worldwide trends, discussing biases in membershipdue to economic status and access to technology.

Finally, we look at the impact of local languages in social network-ing participation. We will investigate how content access in a locallanguage in�uences adoption, and the extent of this in�uence in var-ious regions. As new languages are introduced, we analyze how ita�ects usage in developing countries. To show this e�ect, we considerpairs of countries with the same national language, but di�erent eco-nomic and cultural backgrounds, and explore the correlation betweenlocal languages and adoption.�e rest of this paper is organized as follows. We will �rst look

at the data collection and extraction process, which forms the basisfor the rest of the paper (§.). We will then look at the logistics ofprocessing large amounts of data, o�en measured in several terabytes,in an e�cient manner (§.). �is is followed by the analysis of socialnetworking use in developing countries along several dimensions,which makes up the majority of the paper (§). We will �nish bydiscussing related work (§), and providing our conclusion (§).

2. DATASET�is section describes the process of data collection and data anal-

ysis used throughout the paper. We will �rst discuss the pipeline ofdata collection on the LinkedIn platform, and how data is aggregatedfrom several sources. We will then introduce the data analysis in-frastructure used in this paper, which also supports many LinkedInservices.

2.1 Data collectionWe collect two main types of data at LinkedIn. �e �rst form is

replicated from production databases, which consists of data mostly

provided by LinkedIn members. �is data includes member pro-�le information, their education, and their connections with othermembers.

�e second form is activity-based tracking data, which correspondsto logins, pageviews, and user agents. �is data is aggregated fromproduction services using Kafka [], a publish-subscribe system forevent collection and dissemination developed at LinkedIn. As ofwriting, Kafka is aggregating hundreds of gigabytes of data and morethan a billion messages per day from LinkedIn’s production systems.�e data analysis presented in this paper is done over a large

amount of data, and is intended to represent interesting character-istics of social networking use in developing countries. In order toclassify countries into groups, we use the UN geoscheme for macrogeographical regions from the United Nations Statistical Division [].Member country information is provided during registration.�is scheme is based on the M classi�cation, and is o�en used

for statistical analysis purposes. To better represent socioeconomicdi�erences, we make two common adjustments. First, we group Cen-tral America, the Caribbean and South America into “Latin Americaand the Caribbean” (or “Latin America” for short). In addition, weextract Western Asia (the Middle East) into a group of its own. Whilewe have represented Africa, Asia and Latin America separately inour analysis, we o�en refer to their combination as the developingworld, and compare their statistics with North America and Europe.Figure shows a map representation of our regional classi�cation.

When we represent countries on �gures, we have sometimes usedthe ISO two letter country code for graphics readability. Further,any chosen countries have at least , LinkedIn members so as toavoid skew due to sparsity.

2.2 Analysis InfrastructureOne of the core pieces of data analysis infrastructure at LinkedIn is

Hadoop, an open source implementation of MapReduce []. MapRe-duce provides a framework for processing big datasets on a largenumber of commodity computers through a series of steps that parti-tion and assemble data in a highly parallel fashion, simplifying theprocess of writing parallel programs by providing the underlyinginfrastructure, failure handling, and simple interfaces for program-mers.

At LinkedIn, and for the analyses in this paper, we use MapReduceas well as two scripting languages on top of Hadoop: Pig [], a highlevel data �ow language, and Hive [], a SQL-like language. A�eraggregations are computed on Hadoop, the resulting data is smallenough to be processed by common tools locally on a single machine.

3. DATA ANALYSIS�is section discusses six themes in understanding social network-

ing usage in developing country contexts. As this work is a com-parison of the developing world against the developed world, wenormalize all data to the United States or North America respectively.In a couple of cases, we have had to estimate data, which has beenclearly documented.

3.1 ConnectionsOur �rst topic focuses on the composition of connections in the

social network for members from various regions. A connectionis established when a member requests an invitation with anothermember in the network and is later approved by the invitee. Onlinesocial networks enable participants to establish connections withmembers from around the world, and this section investigates themakeup of these connections. Note that connections are bidirectional,with each connection linking two members in both directions.

An interesting pattern in analyzing social network connectionsis the interconnectedness of members within a region, or the local-

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Figure : Geographic interconnectedness—fraction of connec-tions originating from the same geographic region as members inthat region

ity of relationships. We express the geographic locality of relation-ships by computing the ratio of connections that are established tomembers within the same geographic region. When consideringmacro-geographic classes, we measure the fraction of connectionsestablished within the samemacro-geographic region. We then breakthe numbers down by country, and consider connections within thesame country.Figure shows the interconnectedness of various regions. For

each macro-geographic region represented, we also provide a fewselected countries from the same region, and consider connectionlocality at the country level. Africa and the Middle East have two ofthe lowest rates of geographic locality: nearly % of connections ineach respective region is established withmembers outside the region.Figure shows geographic locality of connections for all countries inEurope and Africa on a map. As the dots on each country get largerand darker, connection locality increases.

One of the important factors in understanding connection localityis the membership population from each region. Intuitively, as thenumber of members in a region increases, the chances of establishinga relationship with similarly located members increases. However,this can be balanced out by the increase in membership of otherregions, which also provides more opportunities for cross-countryand cross-region relationships. We �nd some correlation between thesize of the membership base in a country and the rate of connectionlocality (ρ ≈ ., for countries with more than , members).

Another interesting way of looking at connection distributions isto consider how far connected members are from each other. Usinglocation information, we estimate the distance between membersusing the Haversine function []. Figures (a) and (b) show con-nection distance for macro-geographic regions and a selection ofcountries. For each region, the distance distribution is computedby considering all connections that originate from the region. Onaverage, Africa and Asia have the two longest distances for connec-tions, and this is also re�ected in the individual countries represented.

Figure : Geographic interconnectedness—locality of connectionsfor members in Africa and Europe, computed per country. Biggerdots represent more connections established to members withinthe same country.

Several reasons, including geographic attributes of the region and therate of connection locality, a�ect this distribution.For connections that do not terminate in the same geographic

region, we consider patterns in cross-country and cross-continentalrelationships. Figure is a branching map that depicts where out-bound connections terminate for a few selected countries. For eachcountry, we show a few countries where members in the originatingcountry have connections to. �e thickness of the line for each ar-row corresponds with the fractions of connections that terminatein the destination country. To avoid cluttering, we have removedself loops, instead providing the fraction of local connections as apercentage.

3.2 ActivityMeasuring the activity and engagement of social networking par-

ticipants is an important component in understanding usage fromvarious regions. �is manifests itself in several ways, ranging fromhow actively members are making connections on social networkingsites, to the duration of visits to social networking sites. Several webusage studies in developing country contexts have indicated that usersspend a signi�cant fraction of their online time on social networkingand communication websites [, ]. �is section presents a fewmetrics to further delineate usage across developing countries.An important aspect of social networking activity is the rate of

establishing connections. Much of the utility in social networks isdriven from communicating with fellow members, and connectiongrowth is a key indicator ofmember engagement. Figure (a) presentsthe normalized rate of connection growth for January acrossseveral regions, which is calculated as the average month over monthgrowth in the number of connections for members from each region.�is rate is normalized such that the rate of connection growth forNorth America is one. Figure (b) presents the connection growthinformation for a selection of countries for January , normalizedto give the US a value of one.

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Figure : Outbound connections—a representation of cross-country social network connections for various countries

Connection growth is the fastest in developing regions, whichis also re�ected in the individual countries represented. One of themain reasons for this is the increasing addition of newmembers fromthese regions who are actively making connections on the network.In the early stages of social networking use, members actively addconnections to their network. Even then, however, some regions aremore active in adding connections. For example, during January ,members from Africa are adding new connections quicker than theircounterparts in Latin America, although the membership base isgrowing around % faster in the latter.Another metric we consider in analyzing activity on the social

networking platform is the duration of visits. A session is de�ned asa continuous user activity with an idle period of at least minutesindicating a new session. Figure (a) shows the average normalizedlength of sessions for January . In general, sessions establishedfrom developing regions generally last longer. A key factor for thesedi�erences is network connectivity, which varies signi�cantly for dif-ferent regions. For example, low bandwidth connectivity in Africanand Asian countries requires members from those regions to spend

more time interacting to obtain the same information as North Amer-ican members who have shorter lived sessions.

To better represent this relationship, Figure plots the average andpeak inbound bandwidth from accesses in developing regions onJuly , normalized to North America. �ese measurements areobtained by a system that monitors LinkedIn’s inbound network traf-�c from several endpoints around the world, in part for detecting andpreventing network attacks. �ese numbers correspond to Akamai’sstate of the Internet report [] which estimates average connectivityfrom various regions. As mentioned earlier, the low bandwidth con-nectivity of members from developing regions is one of the reasonsfor elongated sessions.In addition to session duration, visit frequency is an important

metric for comparing social networking engagement across devel-oping regions. Figure (b) plots the normalized, average numberof sessions per member for various regions for January . �edata is normalized such that members in North America have a visitfrequency of . Developing countries generally have a high number ofvisits per member, although this could be skewed by the in�uence of

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Figure : Member activity—the duration and frequency of member visits from several regions

newer members. �is also correlates with the growth in the numberof connections described earlier.

3.3 Access devicesMobile penetration has been one of the singularly most important

factors in connecting developing countries locally and across theglobe. By the end of , there were more than . billion telephonesubscribers around the world, with nearly a billion of them havingaccess to G data services []. �is growth has been largely driven byAsia and Africa, which have the two highest growth rates in the world.Mobile access is available to nearly % of the world population. Anumber of studies in the developing world indicate that for manypeople, the phone is the �rst, and sometimes only, gateway to theInternet [].In this section, we focus on how users access social networking

services from various regions. We broadly divide access verticals tomobile and desktop. Mobile accesses include visits that were directlymade from mobile web browsers, or through applications that accessthe social network over a set of API’s. In addition to smartphoneapplications for platforms like the iPhone, Android, and BlackBerry,LinkedIn also has a native Symbian application that runs on Nokiaphones, which are more common in developing countries. �e frac-tion of mobile accesses is the metric of interest in this case.

For each region, we compute the ratio of accesses made from mo-bile devices. To compute access ratios, we look at the fraction ofsessions that were made from mobile browsers and applications, ag-

gregated by geographic regions. Each ratio is computed as a fractionof accesses from mobile devices in that region to the total accessesfrom the same region on a monthly basis. Each month has beennormalized to the mobile access fraction in North America or theUS, respectively.Figure shows the ratio of mobile sessions by region and select

countries for a period of months, eachmonth normalized to NorthAmerica or the US, respectively. At a regional level (c.f. Figure (a)),Latin America and the Middle East have the highest fraction of mo-bile accesses, and mobile accesses have been increasing signi�cantly.Figure (b) presents a selection of countries in developing regionswith high mobile access ratios. For example, in Africa, Nigeria hassome of the highest mobile access rates with nearly times as muchas mobile accesses from North America.

3.4 Demographics�is section focuses on the age and gender composition of social

networking participants from developing regions. �e age of mem-bers is estimated from their pro�le education information: we assumemembers were years old when they start their career. While thistechnique might be a good approximation for members in westerncountries, a caveat is that its e�ectiveness might vary in di�erent areaswhere career start ages might be generally di�erent.

Generational bias is an important factor in online participation.Perhaps more interesting is that this bias tends to operate on a globalscale. When we look at the average and median ages of social net-

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working members from across the world, participation is naturallyskewed towards people of younger age. �e age distribution is alsointeresting when considering the underlying makeup of the popula-tion in di�erent regions. Countries in developing regions generallyhave a younger population, which is re�ected in the social networkrepresentation of age groups.As shown in Figure (a), the median ages for members from

North America are a few years higher than those in Africa or theMiddle East. �e age distribution for Asia and Latin America isalso quite similar, roughly within a year compared to members inAfrica. Broadly speaking, younger median ages for members fromdeveloping regions correlate with the di�erences in median ages ofthe underlying population. Figure (b) shows a selection of somecountries from each region with median ages in the highest or lowestquantiles.Gender representation is another interesting characteristic for

many regions. We approximate gender information by classifyingmember �rst names using a large annotated catalog retrieved fromseveral baby name books. Names that could not be mapped to agender—the catalog of baby names is biased to Western names—orare ambiguous are labelled “unknown.” In all of the gender infor-mation �gures, we have represented the fraction of users we werenot able to map to genders. �e unknowns are rather high, so anyconclusions should be viewed with some suspicion.

Figure (a) shows the female membership ratio for a few regions.Globally, males are generally overrepresented by membership, butthe di�erences are more pronounced in many developing countries.Many of these di�erences can be attributed to social gender roles andeconomic di�erences. For example, the Middle East has the lowestfemale membership ratio in the world, with females making up lessthan % of the total membership base. �e ratio is slightly higherfor Africa, but with signi�cant di�erences from country to country.Figure (b) shows a selection of countries with various female

representations from several regions. Latin America has one of thehighest female ratios in the world, and several Asian countries havegender representation in line with the general population. However,countries like India and Bangladesh have a highly skewed male rep-resentation. In Africa, South Africa has one of the higher ratios withnearly % female makeup (compared to % in the general pop-ulation []). North African countries share similar traits with theMiddle East with lower female representation compared to the restof the continent.

3.5 Education and Careers�e ��h theme in our analysis focuses on educational levels and

career industries of members. As a professional social network,LinkedIn encourages its members to enter their educational and

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Figure : Mobile Access Growth—the ratio of sessions establishedfrommobile devices, eachmonthnormalized to the fraction ofmo-bile accesses in the US

work history to their pro�les. Education levels can include one ormore user provided description of the member’s educational history.Education levels are described di�erently across the world. For ex-ample a “diploma” in Ethiopia corresponds to a year degree that isequivalent to an associate degree obtained from a community collegein the US. To mitigate this problem, we broadly divide educationlevels to four: high school, college, masters, and doctorate. When amember has listed more than one education level on their pro�le, wepick the highest one. Members must also provide an industry whenthey describe their career. Industry captures a high level classi�ca-tion of career paths, and there are over industries represented onLinkedIn.We consider educational levels in several regions in comparison

to educational makeup in North America. Figure shows the dis-tribution of educational levels in relation to North America or theUS respectively for several regions and countries. With the exceptionof high school graduates, it is interesting to note the near uniformdistribution of members with higher educational levels in several re-gions. Africa and the Middle East have a high fraction of high schoolgraduates in the membership base. When considering EducationIndices from the UN Human Development Report [], we note thatprofessional social networking membership is not representative ofthe underlying literacy rate and education index in many developingcountries. Rather, it is skewed towards to relatively more educatedmembers, which can translates to relative economic a�uence, andimproved access to connectivity.Table shows the top- industries represented from each region.

Some di�erences appear as we look down the list of industries from

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each region. �ese di�erences are more apparent when looking ata selection of countries as shown in Table . As expected, industryrepresentation in a country tends to re�ect regionally establishedindustries, such as oil/energy in Nigeria or computer and so�ware inIndia. In addition to the volume of professional regionally establishedindustries hire, they also tend to have more international contacts,which has an impact on technology adoption.

Another interesting aspect of represented industries in the networkis how members connect across various industries. In Figure , we

look at the industry similarity of connections, which is de�ned ina similar manner as geographic interconnectedness in Section ..For each member, we compute the fraction of connected membersthat also work in the same industry as the member. Interestingly, therate of industry similarity remains very close for all the regions weconsidered, with members having only –% of their connectionsfrom a similar industry. Intuitively, one might have expected mostconnections to remain within the same industry, where professionalrelationships are natural to establish.

3.6 Local languages�e last topic we consider in understanding social networking

in developing regions is the impact of local languages on adoption.LinkedIn is available in a multitude of languages, a few of which arelocal languages for many regions in Africa and Latin America. Wefocus on a few chosen languages that have been available to membersfor at least one year.We �rst look at the the membership growth rate for various lan-

guages. Figure shows a month-over-month time-line from Jan-uary through May of �ve languages normalized to English.�ere is a substantial increase in membership in the language whenit is �rst introduced, which o�en remains high for about six monthsbefore it regresses to the mean.It is o�en interesting to see the impact of local languages on par-

ticular countries. In order to make some comparisons, we choosethree pairs of countries from di�erent regions such that the national

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Region Top industriesAfrica accounting, banking, education manage-

ment, information technology and ser-vices, telecommunications

Asia computer so�ware, education manage-ment, �nancial services, informationtechnology and services, telecommuni-cations,

Europe computer so�ware, �nancial services, in-formation technology and services, mar-keting and advertising, telecommunica-tions

Latin America construction, higher education, informa-tion technology and services, marketingand advertising, telecommunications

Middle East banking, construction, information tech-nology and services, oil and energy,telecommunications

North America education management, �nancial ser-vices, hospital and health care, informa-tion technology and services, real estate

Table : Top industries by region (ordered alphabetically)

language for each pair is the same. �ese include Cameroon andFrance (French), Argentina and Spain (Spanish) and Brazil and Por-tugal (Portuguese). �e top half of Figure shows the average nor-malized month over month growth of languages for –. �ebottomhalf shows the average normalized rate ofmembership growthfor each country, and how the rate changes as languages as introduced.In all of the cases, we observe membership growth responding morepositively for countries from developing regions as the national lan-guages are added. Some of this di�erence can attributed to the overalldi�erence in membership growth across several regions, but the ad-justment in the rate of growth a year a�er the language has beenintroduced indicates that local languages play an important role inearly adoption, and even more so in the developing world.

4. RELATED WORKOne class of related projects come from web usage studies that

provide a macro classi�cation of how users spend time online. �ereare several web usage studies that have been conducted in developingcountry settings [, , , ]. Du et al. [] evaluated HTTP tra�ccaptured from shared access sites in Ghana and Cambodia. �eirresults demonstrate several features of web usage in developing coun-

Country Top industriesIndia computer so�ware, education manage-

ment, �nancial services, informationtechnology and services, telecommuni-cations

Malawi accounting, banking, education manage-ment, information technology and ser-vices, non-pro�t organization manage-ment

Nigeria accounting, banking, information tech-nology and services, oil and energy,telecommunications

Saudi Arabia construction, hospital and health care,information technology and services, oiland energy, telecommunications

United States education management, �nancial ser-vices, hospital and health care, informa-tion technology and services, real estate

Table : Top industries by country (ordered alphabetically)

date

lang

uage

gro

wth

(nor

mal

ized

to E

nglis

h)

1

2

5

10

25

50

100

●

●●

● ●

● ●● ●

● ●● ●

●● ●

●

●

●

●●

●●

● ●● ●

●

●

●

●●

●

● ●

● ●● ● ●

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2008 2009 2010 2011

● BR DE EN ES FR IT

Figure : Locale growth—monthovermonth growthof languages,with a focus on the impact of newly added languages

tries prior to the widespread adoption of social networks. Anotherstudy of Internet usage and performance in Zambia [] points atthe increased adoption of social networking and communicationtools even in rural villages in Africa. Analysis of web usage in Macha,Zambia, some kilometers from the capital city Lusaka, revealsseveral interesting �ndings, including social networking sites as thetop visited destinations. More speci�c web access analysis from aschool setting in India [] also indicate wide usage of email commu-nication and social networking. Our work complements this bodyof work by providing analysis on the adoption and usage patternsof social networking in developing regions. As social networkingcontinues to be a dominant web usage scenario around the world,this paper provides researchers with some insights on the adoptionand characteristics of social networking, particularly in developingregions.

A large scale study of web tra�c using data collected from a worldwide content distribution network (CDN) by Ihm et al. resemblesour work in the scale of data analysis []. �eir work analyzes webcontent that represents one week’s worth of browsing data fromnearlyK users across countries.�ey observe a number of interestingcharacteristics of web usage in developing regions, including thedesire for rich media and di�erences in download type distributions.In contrast, our work focuses social networking usage at a globalscale by using data from over a million members that come fromevery country in the world, with tens of millions of those membersfrom developing regions. We combine individual pro�le information

average month−over−month growth (X)(normalized to EN)

lang

uage

BR

DE

EN

ES

FR

IT

2008

0 10 20 30 40

2009

0 10 20 30 40

2010

0 10 20 30 40

(a) Locale growth

average month−over−month growth (X)(normalized to US)

coun

try

Argentina

Spain

Brazil

Portugal

Cameroon

France

2008

0.0 0.5 1.0 1.5

2009

0.0 0.5 1.0 1.5

2010

0.0 0.5 1.0 1.5

(b) Membership growth

Figure : Membership with languages—the impact of languageintroductions on countries with the same national language, butdi�erent socioeconomic backgrounds

with member activity logs for providing researchers a revealing lookon some patterns and characteristics of social networking usage indeveloping regions.

5. CONCLUSION�is paper provides the �rst large scale and detailed analysis of

social networking usage in developing countries. As Internet accessimproves for users in those regions, online participation has beennaturally increasing. Using pro�le and activity data from LinkedIn, asocial networking site with over a million members worldwide,this paper has presented several themes in social networking usage indeveloping regions. We looked at the characteristics of the nature ofinterconnectedness and geographic locality for members, the activityand engagement level of members in developing regions, as well asaccess verticals for content from various regions. We also discussedthe demographic and educational makeup of members in developingregions, and the impact of local languages in social network adoptionand growth.�e goal of this paper was to provide researchers with a detailed

look on the characteristics of social networking usage in the devel-oping world compared to the rest of the world. As several studiesin developing regions have indicated [, ], users spend a sizableportion of their online time on social networking and communica-tion websites. �is study further explored social networking usage,

further delineating its characteristics in developing regions. Usingdata from one of the largest commercial social networking entitieswith global reach, the paper focuses on several interesting dimensionsof social networking use in developing county contexts.

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