Impression management through people ... - IBM Research · for social media sampling and design Daphne Ruth Raban Faculty of Management, University of Haifa, Israel ... cal and terminological

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Impression management through peopletagging in the enterprise: Implicationsfor social media sampling and design

Daphne Ruth RabanFaculty of Management, University of Haifa, Israel

Avinoam DananFaculty of Management, University of Haifa, Israel

Inbal RonenIBM Research, Haifa, Israel

Ido GuyYahoo Research, Haifa, Israel

AbstractPeople tagging allows a person to tag one’s self or others; it is reciprocal and therefore has social implications. The main uses of corpo-rate people tagging systems are for building internal social networks, solving problems, and seeking expertise. We explored the statisti-cal and terminological relation between self-presentation and perception by others as reflected by the use of tags in a people taggingsystem within a large enterprise.Due to the features of the power law distribution of the data, two different samples were analyzed. Using content analysis, we foundthat when there are few self or social tags, users prefer to use tags from the Environment and Technology categories, providing tagsthat tend to be objective or factual. When tagging approaches saturation, it becomes more subjective and social, using tags from theIndividual category. Self-tags tend to be more factual describing technology expertise while social tags augment the individual tags byadding a personal dimension. The more people tag and get tagged, the more terminological overlap develops. We conclude by provid-ing practical advice on how to create a sustainable system by balancing originality and duplication using interactivity and feedback.

KeywordsContent analysis; impression management; sampling the power law distribution; tagging

1. Introduction

Social activity has become inseparable from content creation in many cases on the Web and in the workplace. While the

use of multimedia is prevalent, text is commonly used to portray or interpret the characteristics of application users. The

focus of this research is on impression management in the workplace as revealed by the analysis of textual tags used to

manage or describe an impression about one’s self or colleagues. We compare self-presentation with the perception by

others and we also compare two user samples: highly-active and average users.

Impressions online can form based on implicit (expressions one gives) or explicit (expressions one gives off) cues as

people present themselves on the Web. A particularly explicit form of describing people is a people tagging system

Corresponding author:

Daphne Ruth Raban, Faculty of Management and the Center for Internet Research, University of Haifa, Mount Carmel, Haifa 3498838, Israel.

Email: [email protected]

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implemented in large organizations. In such systems people describe themselves and their peers by the use of tags. This

offers a unique opportunity to study explicit self-presentation and perception by others using real-life data.

The uniqueness of people tagging, unlike tagging other online content, is that people tagging is reciprocal [1]: A per-

son can tag or be tagged. Tagging people can be used for building social networks, solving problems, and seeking exper-

tise [2, 3]. Using tags in order to describe people or colleagues can expose information about skills, roles, projects, and

more, which can be helpful for all other users in the enterprise.

The paper begins with background about impression formation, people tagging, and participation patterns in online

systems. We then report the results of a study about the difference between self-presentation and perception by others

based on data from a people tagging application in a large enterprise. Using content analysis, we compare average users

to highly-active ones. Finally, we provide ideas for improving the sustainability and usability of the people tagging

system.

1.1. Impression formation

How people present themselves and how they perceive others are questions that challenged social psychologists ever

since the pioneering work of Solomon Asch [4]. Impressions are quick to form and fairly stable over time [5]. In order to

be socially accepted, people tend to try and control the information they present about themselves in one-to-one encoun-

ters as well as in group meetings, or before large audiences.

People generate impressions by two kinds of self-expression: expressions one gives, and expressions one gives off

[6]. The first involves relatively easily controlled, presumably intentional expressions, conveyed through traditional ver-

bal communication [7, 8]. The other kind is considered to be more theatrical and contextual, non-verbal, and presumably

unintentional.

In social spaces individuals may try to control the impressions others receive and will calculate behavior so that the

audience will believe what they see [6]. In order to create an impression, individuals will use explicit expressions such

as physical appearance and verbal cues as well as implicit expressions such as body language and non-verbal cues.

Jones [9] described two major strategies of impression management used for attainment of power: ingratiation and

self-promotion. Ingratiation is a strategy used by a person who wishes to be liked by others and does not typically involve

conscious awareness of deliberate planning. Ingratiation is reactive, done in response to other people’s communication,

and is commonly characterized by opinion agreement, compliments, favors, warmth, understanding, or compassion. Self-

promotion is a proactive process for generating favorable opinions regarding a person’s competence. Typical examples

include self-promotion in relation to getting accepted to an academic program or a new work place. When done in excess,

self-promotion may be perceived as intimidating, even off-putting, thus creating undesired outcomes. The people tagging

system researched here is a special opportunity to study self-promotion and compare it to the perceptions generated by

others.

1.1.1. Impression formation online. Any online activity is an opportunity for self-presentation. Common examples include

the construction of a personal home page, the introduction one is expected to make when entering online groups, the

descriptions in various social media sites or the profile one accumulates for oneself willingly or not on a variety of online

systems [8, 10].

By using built-in system profiling features or by creative content management, participants choose how to introduce

themselves, how to manage their portrayed image. As a result, some of the descriptions may be inaccurate or incomplete

[11]. The importance of accuracy of the description varies depending on the tool and the use of this information by

others.

In social network sites (SNS) it is more complicated to fake information or to have different personalities because in

SNS the norm is to have friends. These friends are exposed to the information presented in the profile and if the informa-

tion is inaccurate they might express doubts on the validity of this information or the system as a whole. Therefore, exis-

tence of friends may confirm the self-presentation of the profile owner [12]. This is certainly expected in organizational

social networks intended for professional purposes.

Internet users visit SNS and create strategic profiles to influence how others perceive them. While doing so, the pre-

senter creates an image to the receiver which may have varying degrees of truthfulness. Given that the presenter chooses

what to present and even who can view the profile page, and given that s/he has goals to achieve, users should be con-

scious of their friends’ activities and the ways in which they choose to respond. The illusion created by online impres-

sion can fade away when a face-to-face meeting eventually occurs [11].

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Journal of Information Science, 2016, pp. 1–21 � The Author(s), DOI: 10.1177/0165551516636305



Perception of others online is constructed according to the explicit and implicit cues the other manifests. Each conver-

sation or posting online contains both the carefully controlled, explicit, cues and the unintentional, implicit, cues. In order

to make a positive impression a person will carefully select the explicit cues to manifest. The online perceiver is aware of

that and he is likely to notice the implicit cues as well. This research will examine both sides of impression management:

self-presentation and perception by others.

1.2. Tagging

Users of tagging systems use tags to contribute to a free text taxonomy characterizing web pages, photos, products, and

other types of online content [2]. Tags enrich the information about an item and can be used for later retrieval. Since tag-

ging is usually done by end-users, the result is the creation of a web of human topical interest.

Tags can be chosen from a controlled vocabulary or they can be free text assigned by the user. When free text is used,

the resulting metadata can include homonyms, synonyms, spelling mistakes, and errors that can lead to inappropriate con-

nections between items and insufficient outcomes for information searches. On the other hand, with free text the user can

use terms he thinks are appropriate to describe or help him recall information without the burden of selecting a category

from a known taxonomy. Free text also allows for a dynamic update of the vocabulary, maintaining its relevance.

Collaborative tagging systems present new challenges to system designers because social and psychological factors

may affect users’ level of activity and tag choices. One factor that is particularly applicable is the principle of social proof

(also known as social influence), which indicates that people model their behavior based on others. This principle sug-

gests that: ‘We view a behaviour as correct in a given situation to the degree that we see others performing it’ [13].

1.3. People tagging in a workplace

The special case where the resources to tag are people, is referred to as people tagging. A unique quality of people tag-

ging in contrast to other tagging applications is that tagging is reciprocal: a person can tag or be tagged. From the indi-

vidual’s perspective, such systems can be described as a social process, enabling use for acting and reacting, posting, or

replying [14].

Tagging people offers a new dimension for SNS, and specifically social networking applications within enterprises.

Implementing a people tagging system within an enterprise weaves a social network where employees can categorize and

characterize their colleagues, aiding better recall, solving problems, and seeking expertise [3].

Ehrlich and Cash [15] claimed that people in an enterprise can provide valuable expertise to solve problems.

Knowledge work often involves finding opportunities to contribute to collaborative work. Possibly the simplest way of

finding those opportunities is to promote one’s skills to other members of the organization is by the use of tags [16].

People tagging systems are commonly used for contact management. As with social bookmarking, people tagging

enables users to organize their contacts into groups, annotate them with terms supporting future recall, and search for

people by topic area [1]. It helps people to find, learn about, and keep track of each other in order to improve the effec-

tiveness and reduce the cost of forming and maintaining professional relationships.

Using tags in order to describe people or colleagues can provide employees’ skills, roles, projects, and more informa-

tion that can be helpful for users in the enterprise. Furthermore, people tagging can provide a picture of one’s social-

professional intra-organizational network [17]. The combination of the description and the social network could yield

even more compelling results and enhance the reputation of some users. Beyond contact management, people tagging

enables the formation of an organizational community that collectively maintains each other’s interests and expertise

[18].

The earliest known collaborative tagging system was ‘WebTagger’, which was implemented in 1997, created by a

team at NASA’s Ames Research center [19]. WebTagger included a relevance feedback mechanism to rank the tags

applied to bookmarks based on previous relevance judgments made by the users. Unlike any other tagging system,

WebTagger provided an explicit vocabulary for tagging and not an open one [20].

IBM’s Dogear illustrates how such a system can be adapted to the enterprise [21]. Dogear supports bookmarks of

Internet and intranet information sources, and provides user authentication via corporate directories [22].

In the past few years some enterprises discovered the advantages such applications can contribute and integrated the

people tagging feature within enterprise applications. As a solution for the private tags problem mentioned above, Razavi

and Iverson [22] explored OpnTag where participants can enhance privacy in social tagging systems. The access control

feature added to the people tagging application enables users to categorize their friends or colleagues into groups and to

decide what information to reveal to any group. Another enterprise people tagging system was Fringe Contacts intro-

duced by Farrell and Lau [2]. Fringe allowed employees to update personal profiles on their own, or by others tagging

Raban et al. 3




them with any kind of term so that they provide vital and vast information on their interests, expertise, group affiliation,

personal characteristics, and so on. Farrell and Lau showed that the purpose of participating in social bookmarking of

web pages is similar to the purpose of participating in people tagging systems. Users tag people for personal benefit (self-

presentation or contact management), but when the system becomes popular, a folksonomy of employees is created that

can be highly useful in mapping employees’ interests and expertise. The use of Fringe Contact system removed the

dependence on the users to update their formal profiles and the advantage for the enterprise is augmentation of profiles in

a corporate directory.

Research on Fringe Contacts revealed the accuracy of the information and the purpose for contribution. Farrell et al.

[1] used interviews in order to understand whether the tags are accurate and whether the users use the system appropri-

ately in order to find out if any change in the system method or features is required. That study showed that the tags

describe the employees’ interests and expertise accurately. In addition, it showed that the employees use the system

appropriately and no offensive or inappropriate use of tags occurred. This finding may be attributed to use of the system

within an enterprise environment where the users are colleagues and they understand the advantages of correct informa-

tion for them and for the enterprise. Another study by Farrell et al. [3] examined the purpose of use and participation in

the system and showed that an active minority of Fringe Contacts users is using people tagging for building

communities.

Two studies compared self-tagging and tagging of others within an enterprise people tagging system based on the

Fringe Contacts’ database. Results showed that people put more effort into tagging themselves than into tagging others

meaning that users who tag themselves contribute the most to populating the system and making it relevant [14].

Furthermore, while self-tags tend to be more factual describing technology expertise, social tags augment the individual

tags by adding a personal dimension [23].

1.4. Participation patterns

Participating in online groups often follows a power law distribution where a minority of participants actively contributes

and the majority remains receivers [24]. According to Nonnecke and Preece [25], lurkers make up over 90% of online

groups.

In social systems, the users can act or react, post, or reply. The participation ranges from complete inactivity to full

activity. Based on [14] we define the ‘participation continuum’, which represents four levels of contribution (as shown

in Figure 1):

(1) Active\initiate – a participant who initiates discussion or interaction, acts, and reacts in the interaction.

(2) Active\respond – a participant who responds/reacts to others and does not initiate interaction.

(3) Passive\lurk – a participant who only reads others’ messages. This kind of participant neither acts nor reacts.

(4) Passive\inactive – a participant who is registered in a system but does not actually use it and does not even read

others’ messages.

Each participant in social applications belongs to one of the four levels defined above. The dilemma for individuals is

to either contribute to the common good or to shirk and free ride on the work of others [26]. System designers prefer the

users to be more on the active side and to continuously contribute to the system and the organization. This contribution

may be either by duplicating existing tags (social proof) or by using new tags that will bring new information to the sys-

tem. In the corporate tagging system, as in most SNS, overall participation is expected to follow the power law distribu-

tion with mostly passive users.

Figure 1. The continuum of participation in social technologies.

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This research will focus on two groups of active users: highly-active and average. From a methodological perspec-

tive, the average user group is a balanced representation of the whole active population. From a system development

perspective, due to the power law participation distribution, it is interesting to study the group of enthusiasts who are

more dedicated to the system in order to explore the highest level of activity and think of ways to induce regular users

to become more engaged. Using the database of a people tagging application implemented in a large enterprise, and

applying two different and complementary sampling methods we explore the following question: What terms do people

use to tag themselves (presentation of self) compared to terms used by others (perception by others)?

2. Method

Content analysis was applied to two large samples taken from data harvested from a corporate people tagging application.

In the following we describe the system and data, two sampling methods and the content analysis procedure.

2.1. System and data source

The data were harvested from Fringe, an organizational application within IBM [1]. Fringe enables employees to tag

others or themselves with free text tags through a single text box. Multiple tags can be provided either comma separated

or through multiple insertions into the text box. There is no categorization or auto-completion of tags. As described in

[1], the design principles included no need to ask for permission by the tagged employee in order to enable a lightweight

application.

Each employee has a profile page which shows three tag clouds as shown in Figure 2: the tags the employees tagged

themselves with (self-tags), tags others tagged the employee with (social tags), and the tags the employee tagged other

employees with. The size of each tag in those clouds indicates the relative number of times the tag was used. Hovering

over a tag reveals the people that used the tag on the particular employee. Thus, though there is no system initiated feed-

back on the usage of repeated tags, a user can inspect previous tags used on or by the employee and be influenced by

those. This open display of all tagging interactions enabled a system of ‘social translucence’ [1] which causes people to

carefully consider their tagging behavior and reputation.

Following its internal usage as a research project, many of Fringe’s features were implemented in IBM’s social plat-

form, IBM Connections. Its main features were the ability to tag people with free text tags, view the tags a person was

tagged with, and view the tags a person used as tags. One could search for all people that were tagged with specific tags.

The system was deployed in IBM for many years.

Our data represent a period of 3 years during which the application was deployed on the organization’s intranet. The

application had 62,332 distinct participants who tagged themselves and others with one or multiple tags. Each employee

had a profile page, which exposed in addition to personal details, the self-tags he assigned to himself and a tag cloud of

the tags assigned to him by other employees (social tags).

Every tagging action was registered in the system with the necessary meta-information, including the employee id,

employee name, and date. For our analysis the tags related to a person were extracted by querying the database through

SQL. The data were subsequently anonymized.

2.1.1. VariablesSelf-tags: tags assigned to an individual user by himself, representing the presentation of self.

Social tags: tags assigned to an individual user by other users, representing the perception by others. Each unique

social tag was counted once even if it was used by more than one person to tag the same peer.

2.1. Sampling

The database of the application was downloaded and arranged in a relational table containing the self and social tags

associated with individual users.

The data follow a power law distribution which is typical of many network-based activities such as web site links and

social networks [27], electronic markets [28], discussion groups [29], and more. The people tagging data are character-

ized by a small number of highly active participants having many self and social tags, and a long tail of occasional parti-

cipants. Taking a random sample would result in a large proportion of dormant users. In order to examine impression

management for highly-engaged users and for average users we analyzed two complementary data samples based on the

definitions in section 2.1.1:

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Sample 1 – highly active sample – participants with a minimum of 20 tags: at least 10 self-tags and 10 social tags.

There are 281 such users with a total of 9506 tags.

Sample 2 – representative sample – since random sampling in a power law distribution will yield mostly people with

low activity, we prepared a cross-tabs table (self vs. social tags). For example, the first cell of the cross-tabs table

contains the number of system users who had one self-tag and one social tag. There were 360 such users. Sample 2

was created by taking 10% of participants from each cell with at least one tag of each kind. This amounted to 473

users with a total of 6547 tags. The overlap between the samples is 28 users.

2.3. Codebook development

The codebook is based on preliminary work of three independent coders in which each coder categorized tags from a ran-

dom sample of 25 users (not included in the above samples). Each coder described the tags according to his/her under-

standing. After examination and discussion common tag classes were formed. These classes fit into three categories:

technology, environment, and individual. Each category contains six classes as detailed in Table 1. Technology refers to

information systems developed or used by the employees. Environment refers to the employees’ social and geographic

circumstances. The Individual category covers personal details.

Figure 2. Fringe sample screenshot showing self and social tag clouds.

Raban et al. 6




Having an agreed codebook, coding commenced with each of the three coders coding a sample of tags. Training, cod-

ing, and discussions took place until an agreement (calculated as inter-coder reliability) was achieved. With a reliable

codebook and coding process we coded all the tags associated with the two samples described earlier.

2.3.1. Inter-coder reliability. The inter-coder reliability test was conducted for the three coders by using a new random sam-

ple of 30 system users. Self-tags and social tags were coded. The three coders worked jointly for the first nine participants

to reach an agreed understanding of the use of the codebook and then each coder coded the remaining 21 participants. A

total of 175 self-tags and 434 social tags were coded.

Inter-coder reliability was calculated using Krippendorff’s alpha index since it is the only index that allows more than

two coders. Krippendorf’s alpha for the three coders was 0.75 for self-tags and 0.73 for social tags. Both values are above

the threshold of 0.7 needed in order to determine the coding process as reliable.

3. Results

3.1. Tag volumes and ratio

Sample 1 (highly active users) contained 4569 self-tags and 4937 social tags. Each user had 16.26 self-tags on average,

and 17.57 social tags. Sample 2 (average users) contained 3513 self-tags and 3034 social tags, on average, each user

tagged 7.42 self-tags and was tagged by 6.43 social tags.

Table 1. Codebook categories, classes, and examples.

Category Class Description Examples

Technology Informationtechnologies

Information technologies andsystems

Linux, android, ooa, eclipse-plugins

Internettechnologies

Internet programming,technologies and systems

Web20, secondlife, atom, javascript, 3dinternet

Theme Broad topic Collaboration, knowledge-management, legacy-transformation

IBM product Products developed and soldby IBM

Websphere-commerceLotus-notes, Lotus-dominoWebsphere-portal, Sametime, Quickr

External product Products not developed orsold by IBM

Photoshop, Mac, iPhone

Project Projects (not products) ofIBM

Project-zero, p-vistaoasis, Bluepedia

Environment Group affiliation Groups such as conferencesand internal or externalevents

cscw2011, web20forbiz, helsinki-2007, class-of-78, Vienna-team

Organizational group Departments andorganizational groups at IBM

Research, sales, services, corporate

Country Country names Norway, CanadaCity City names Hamburg, CambridgeCustomer Customer and partners of

IBMPhilips, Miele, Rails, Citybank

Organization Organization names whichare not customers orpartners of IBM

BMW, Cisco, Apple, Daimler, Teva

Individual Name Name, last name, nickname Bob, LaszloTraining Academic or professional

degree, training, institutionMBA, Certified-IT-Specialist, Miami-University

Job description Description of jobresponsibility

Architect, presales, manager, designer

Special skill Work-related skills Hacker, mentor, demo-guru, speaker, bilingualHobby Non-work-related hobby or

interestSkiing, karaoke, photography, rocker, piano,soccer-fan

Personal adjective Personality-relatedcharacteristics

Leadership, traveler, creative, funny,supergenious, storyteller, recycler

Raban et al. 7




Table 2 shows the mean number of self-tags and social tags in each category and the ratio of means between self-tags

and social tags.

Technology had the highest number of self and social tags and was the only category where the ratios of means

exceeded unity, i.e. people tagged themselves mostly with technology tags, and were tagged by others in the

Environment and Individual categories.

Appendix A provides the full results of the mean number of self-tags and social tags in each class and the ratio of

means between self-tags and social tags (observations with high values are in bold).

3.2. Overlap analysis

Overlap analysis measures the agreement between self-tags and social tags per user. This is useful for assessing similari-

ties and diversity in tagging. Similarity informs the tag content validity while diversity is desirable for a rich description

of the tagged person. Overlap is analyzed across the categories and classes. Overlap in self-tags is defined as the percent-

age of overlapping tags out of all self-tags. Overlap in social tags is defined as the percentage of overlapping tags out of

all social tags. For example, if a user has 50 self-tags, 100 social tags, and of those tags, 20 are found in both types of tag,

then the overlap in self is 20/50=0.4, and the overlap in social is 20/100=0.2. We examined the overlap across the users.

Figure 3 shows the overlap measure in both samples across the three categories: Technology, Environment, and

Individual. Figure 3 reveals an inconsistency in overlap between the two samples. While the Technology category

behaves similarly in both, the Environment and Individual categories vary. The explanation lies in the type and order of

tagging. Self-tags are often Technology tags. In addition, self-tags are usually assigned before social tags, since social

tags tend to be reactive [14]. The Technology tagging process is likely to reach saturation and stability before the

Environment and Individual categories, and this is reflected in the variability seen in Figure 3. The overall overlap found

in this study is in the range of 18–36% roughly, whereas in a recent study about book tags overlap was in the range of

approximately 12–20% [30].

3.3. Tag popularity analysis

This part focuses on the most popular tags in each category and class and examines the level of agreement between self-

presentation and perception by others. Table 3 can be used for comparing self and social tags.

Table 2. Mean tags per user in the three categories for both samples.

Sample 1 (Highly active users) Sample 2 (Average users)

Technology Environment Individual Technology Environment Individual

Mean self-tags 9.15 4 3.11 4.19 1.72 1.51Mean social tags 8.02 5.03 4.52 3.07 1.80 1.56Ratio of means (self/social) 1.14 0.8 0.69 1.36 0.96 0.97

Figure 3. Overlap between self and social tags across the three categories in both samples.

Raban et al. 8




Table 3 offers a unique view in order to compare Sample 1 to Sample 2. The comparison shows that the smaller sam-

ple, Sample 2, contains 60–70% of the results of Sample 1 within the same type of tag and category, with the exception

of self-tags in the Environment category where prediction reaches only 40%. Sample 2 can be seen as a preview or pre-

dictor of Sample 1. Looking at all the top 10 tags, self and social tags, prediction (similarity) stabilizes at about 70%. If

one is interested only in the general direction of the system activity and user profiles, then the parsimonious approach

would be to code the smaller sample and assume 70% accuracy.

Examining the correlation between the mean number of self and social tags and class overlap we observe that in

Sample 1 classes with a higher mean number of tags (self and social) have higher overlap (see Figure 4), while in Sample

2 the correlation between mean tag number and self and social tag overlap is weaker (see Figure 5). In addition, classes

with narrow available vocabulary, for example ‘Organizational Group’ or ‘IBM Product’, have high overlap. Finally, the

results in Figures 4 and 5 are compatible with the social proof theory, meaning that the more tags the more overlap and

convergence.

Comparing self and social tags (presentation vs. perception) can be achieved using the tables in appendices A and B.

Overlap at k (ovl@k) is defined as the portion of tags that are among the k most popular self-tags as well as the k most

popular social tags. For example, an ovl@5 of 0.6 indicates three overlapping tags within the five most popular. ovl@1

directly indicates whether the most popular tag is identical for self and social tags.

Appendix B shows the 10 most popular tags and the overlap in each of the categories, with overlap @10 in the range

of 0.7–0.9. Overlap @1 is 1 for Sample 1 (highly active) but not for Sample 2 (average), indicating a convergence pro-

cess that takes place as tagging increases. Appendix B shows the most popular self and social tags and the overlap in

each class. Name and Training were excluded in this sample since they did not have enough tags for analysis. Overall,

the ‘Individual’ category had the most variance in overlap indicating that this category and its classes may be the point

Table 3. Top 10 most popular self and social tags in each of the categories (Samples 1 and 2).

Top 10 self-tags Top 10 social tags

Technology Sample1 Sample2 Sample1 Sample2web20 Secondlife web20 SecondlifeSecondlife web20 Collaboration web20Collaboration Portal Secondlife CollaborationConnections Soa Portal PortalPortal Collaboration Connections WorkDomino Java Socialnetworking LinuxInnovation Quickr Work SoaQuickr lotus-connections Innovation InnovationSocialnetworking Linux Domino social-computingSametime Domino Soa Quickr

Environment Lotus Vuccore Lotus Lotusweb20forbiz web20forbiz web20forbiz web20forbizVuccore Lotus Swg SwgBlueiq Websphere Sales GbsWebsphere Rational Gbs webspheretaggingsummit2006 Tivoli Issl RationalGbs Sales Websphere IsslSwg Marketing Blueiq StrategyIssl Swg Strategy SalesDesign Strategy Hamburg Tivoli

Individual Blogger Architect Blogger tech salesArchitect Blogger tap-innovator Bloggertech-sales it-specialist Architect tap-innovatorMentor tech-sales tech-sales architectEvangelist Manager Innovator managerCommunity builder Mentor Evangelist Mentorearly-adopter Compliance early-adopter DeveloperManager Communitybuilder Manager portal-expertInventor Inventor Mentor Inventorportal-expert Portal-expert Competitive Hacker

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of interest for studying the reflection of individual differences in impressions, whereas ‘Technology’ and ‘Environment’

may be more useful for studying convergence and agreement regarding impressions.

4. Discussion

This study set out to explore the terms people use to tag themselves (self-presentation) compared to the terms used by

others (perception by others) in a corporate setting based on data from a people tagging system. Because the data follow

a power law distribution, it was not clear how to sample it. We opted to analyzing two samples. Sample 1 consisted of

the most active users, and Sample 2 represented the entire population using a proportional sample from a cross-tabs table

of self vs. social tags. Sample 1 represents a full portrait of the users contained in it, since the number of tags per user in

this sample is larger than the tag saturation point of 12–14 tags [14]. Beyond the saturation point new information is

scarce and incoming information in the form of new tags is most likely to duplicate known information. Sample 2 is a

smaller, and therefore a more economical, sample, however, it provides the situation prior to saturation and in this regard

its results can be perceived as indicatory or transient.

The following discussion seeks to interpret the results by referring to three angles: impression management, sampling

power law distributed data, and implications for system design.

4.1. Self-presentation vs. perception by others

The most active tagging category is Technology. Most of the users prefer to tag themselves with Technology tags, then

with Environment tags, and finally with Individual tags. For social tags this tendency remains, however, the Individual

category is more populated in Sample 1, suggesting that Individual-type social tags are practiced by the more enthusias-

tic system users. This is also an indication that the social process takes time to build. Self-presentation starts by the more

Figure 4. Correlation between mean self and social tags and overlap in popular tags (Sample 1) (r = 0.67, P < 0.01).

Figure 5. Correlation between mean self and social tags and overlap in popular tags (Sample 2) (r = 0.41, P < 0.01).

Raban et al. 10




factual, Technology, tags and later proceeds to the social and personal descriptions. Both are active forms of participa-

tion (Figure 1), however, self-promotion involves initiation while social tagging is likely to be responsive.

The self-to-social ratio shows that people are more comfortable tagging themselves with technology tags (self-to-

social ratio > 1). On the other hand, peers are more likely to use tags from Environment and Individual categories, add-

ing social and personal dimensions to the tag cloud. Self-to-social tagging ratios were high in tag classes that involved

private knowledge, for example, an employee would know more about external products she uses than might be known

to her peers. Classes that attracted considerable activity (marked in bold in Appendix B) were attractive to self and social

taggers alike. This is an example of social proof, people imitating others’ behaviors by conscious copying, since auto-

complete was not available. Based on [14], we may speculate that self-tagging induced social tagging, however the oppo-

site direction could also work. The direction of imitation is secondary in importance to the observation of the existence

of social proof in the people tagging system, i.e. the duplication of existing tags.

The extensive overlap between the top self and social tags seen across categories and classes provides further support

to the social proof concept. In a hypothetical system one might expect zero overlap since overlap reflects duplication of

information, a redundancy. Why do people tag others by tags that have already appeared? To some extent this may rep-

resent oversight – people may not read the full profile of others before tagging them – they might employ certain social

tags as aids in contact management. However, the large extent of overlap present in our data indicates that duplicate tag-

ging is systematic, and therefore is likely to be an outcome of social proof. Social proof may offer the advantage of vali-

dating the initial tags, thereby reducing the negative connotation of redundancy. Yet, another question emerges: what is

the right balance between duplication and originality and how can system designers obtain this balance? We address this

question in section 4.3 about implications for tagging systems. Next, we compare the outcomes from the two samples in

this study.

4.2. Comparing Sample 1 and Sample 2

Generally, users behave similarly in both samples with respect to the three parameters, tag volume and ratio, overlap

analysis, and tag popularity. Sample 1 represents the potential level of activity which is currently practiced by a small

fraction of the users, the ‘head’ of the power law curve, 281 users. The present findings indicate that Sample 2, the aver-

age users, reflects similar results providing a good indication for the results had the system been able to attract higher

activity levels. From a research economy perspective, or an Occam’s razor approach, this is encouraging since despite

the extreme shape of the power law curve, a parsimonious representative sample is sufficient to learn about the users’

activity and content production. Note that the meaning of ‘representative’ in the current work is a constant proportion

from a cross-tabs table of the full population. This is our recommendation for a representative sample from a power law

curve as opposed to a completely random sample. A random sample would be highly skewed toward users with low

activity.

Sample 1 is larger by a factor of 1.45 in terms of the number of tags analyzed. The users contained in it had more than

double the average number of self and social tags compared to Sample 2. Yet, it is important to observe that more social

tags are available in Sample 1, so if the social aspect is important for analysis, Sample 1 should be the focus.

Technology tags were most useful for self-presentation as well as for social tagging in the corporate people tagging

system, followed by Environment, and by Individual tags. The prominence of Technology tags may be related to the

company’s line of business so that in other companies, the technology tagging might be replaced by the particular com-

pany’s area of activity. Interestingly, the use of Individual tags as social tags was more pronounced in Sample 1, implying

that if system designers are interested in personal descriptions, they need to devise a way to encourage social tagging.

Most of the differences (Table 3 and Figure 3) are explained by the nature of the two samples, meaning that when the

volume of self and social tags is low, users prefer to use tags from Environment and Technology categories rather than

from Individual (see Table 4). In Sample 2 there are 116 users (25% of the sample) with one self-tag and 105 users

(22%) with one social tag. Analyzing these specific users indicates that for self-tags, Environment is the most active

Table 4. Mean tags per user in the three categories for users with one self-tag or one social tag (sample 2, N = 221).

Mean self-tags Mean social tags Ratio of means (self/social)

Technology 0.29 0.52 0.56Environment 0.58 0.34 1.68Individual 0.09 0.11 0.75

Raban et al. 11




category and for social tags it is Technology. In addition, Environment is the only category in which the volume of self-

tags is higher than the volume of social tags.

In order to obtain another angle for comparing samples, the data of the most active people of Sample 2 were ana-

lyzed. This sub-sample is similar to Sample 1 in that it contains users with 11 or more self-tags or 11 or more social tags.

The data reveal that for self and social tags when the volume of tags is high, the use of tags from the Individual category

increases. It is even stronger for social tags, where the mean of Individual tags is higher than for Environment.

Technology is still the most active category.

Comparing the results for infrequent and frequent system users within Sample 2 (Tables 4 and 5) indicates that the

choice of tags is random for infrequent users, while the frequent users resemble the activity of Sample 1 more so than

the entire Sample 2 data presented in Table 2. Overall, it seems that many people try to use the system by assigning a

random tag, some of them choose to continue using the system and then their tagging choices indicate awareness of the

existing tagging practices. To economize on research resources, our advice would be to follow our average users sam-

pling technique (Sample 2) and then focus the analysis on the most active users in this sample as seen in Table 5.

4.3. Implications for tagging system design

Terminological overlap in Sample 1 was 29%, on average. Assuming that this sample reached tag saturation, we infer

that a ratio of approximately 70:30 original to duplicate tags is a reasonable outcome for any social tagging system seek-

ing to balance originality with validity to produce a wealth of information about the object of tagging. The overall recom-

mendation of 70:30 may be too coarse. Possibly for the Individual tags a different ratio (such as 85:15) might be desired

in order to extract a higher rate of unique tags. Further fine-tuning of specific classes may also be considered.

System designers may want to direct users to the recommended ratio by providing a relevant statement within the sys-

tem or by developing a corresponding feedback mechanism indicating whether a given tag is duplicate or original and

what tag would improve the tagging process following. Merely adding such a feedback mechanism may have another

consequence which is to induce further activity. Feedback would render the system interactive thereby encouraging some

of the passive users to become active. Specific feedback such as inviting tags of a particular class could be implemented

to enrich the tag cloud. Another mechanism that may induce activity is the provision of a controlled vocabulary while

indicating that free-text is equally desirable. A controlled vocabulary would aid in validation by avoiding typos and might

aid in variance by suggesting additional terms. The system could also be enhanced by adding a timeline to the tagging

process enabling a sort of personal history to be formed. This could inform the company and system users about career

paths.

The corporate people tagging system design described in section 2.1 reflects a philosophy of providing an open and

flexible platform. Possibly, the complete freedom provided to users might lead to low user activity. Our interpretation of

the results leads us to recommend that crowd-based systems should contain a moderate level of prompting and feedback

or even a careful selection of gamification elements in order to strengthen participation as well as community aspects of

such systems. The frequency of prompting could be a function of activity so as to target only people for whom a nudge

might be effective.

4.4. Limitations and future research

The strength of the current study is the employment of an unobtrusive research method in order to learn about activity of

users in their natural work environment. However, there are questions that remain unanswered with the current research

approach and would benefit from follow-up research using questionnaires and interviews. For example, asking users for

the reasons they select specific tags for themselves and for others could lead to deep insights about impression manage-

ment strategies. The users in the study originated from different units within the organization, such as Sales, R&D,

Table 5. Mean tags per user in the three categories for users with 11 or more self-tags or 11 or more social tags (sample 2,N = 150).

Mean self-tags Mean social tags Ratio of means (self/social)

Technology 13.49 9.96 1.35Environment 4.78 5.24 0.91Individual 4.31 5.65 0.76

Raban et al. 12




Services, and Corporate. Inspecting different tagging behaviors across organizational units could lead to additional

insights on tagging behavior in a large enterprise. Interviewing sporadic users regarding why they use the system infre-

quently could lead to developing a more engaging interface. The people tagging system is a good choice for conducting

future user studies and usability assessments. The interest here, however, focused on the social aspects of use more than

on design aspects.

4.5. Conclusion

While Sample 2 is more economical to analyze, Sample 1 provides a richer view of self-presentation and perception by

colleagues at work. Tagging systems are often referred to as ‘social media’, yet the present results show that social pro-

cesses in the system occur only after considerable individualistic activity. Most users are passive beyond sporadic partici-

pation. To economize on research resources, our advice is to follow the average users sampling technique (Sample 2) and

then focus the analysis on the most active users within this sample.

Self-presentation tends to be factual, focusing on Technology tags whereas perception of others, social tagging, pro-

vides the social and personal tags. As the tagging proceeds, social tagging increases and the information about each user

deepens. Assuming that system designers and organizations are interested in more than mere contact management or

self-promotion, they should develop interactive functions in the people tagging system in order to encourage persisting

participation. Higher engagement will lead to richer descriptions and higher system value.

Acknowledgements

Ido Guy participated in this research while working at IBM Research.

Funding

Supported by an IBM Open Collaborative Research grant.

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Raban et al. 15




Appendix B. Top 10 most popular tags and the overlap in each of the sub-categories.

Sample 1

Top 10 self-tags Top 10 social tags ovl@1 ovl@5 ovl@10

Information Tech Linux Linux 1 0.6 0.6Java JavaCss Visualizationlinux-desktop linux-desktopRfid GreenArchitecture Architectureinformation-architecture TweUml ActionscriptActionscript complexity-methodGreen Css

Internet Tech web20 web20 1 0.8 0.7Secondlife SecondlifeSocialnetworking SocialnetworkingSoa SoaAjax second-lifeWeb WebJavascript web-20open-source Ajaxj2ee MetaverseMashup open-source

Theme Collaboration Collaboration 1 0.8 0.8Innovation Innovationsocial-networking Worksocial-computing social-networkingsocial-software social-computinguser-experience user-experiencevirtual-worlds KmMobile MobileUsability social-softwareKm Socialsoftware

IBM product websphere-portal lotus-connections 0 0.8 0.9lotus-connections Sametimelotus-notes websphere-portalDomino lotus-notesQuickr DominoSametime QuickrLearning WplcOdw LearningThinkplace OdwTap Tap

External product Twitter Twitter 1 0.6 0.4Eclipse MacMac MoleskineUbuntu UbuntuMacintosh WebsheetsIphone EclipseVmware foxray-xboundExchange mac-at-ibmPhotoshop AbbSuse Audacity

Project Dogear Beehive 0 0.4 0.5Bluepedia BluepointsBeehive EtsBig-green best-of-bluecollaborationcentral DogearEagle collaborationcentralEts Ebobest-of-blue luana-related

(continued)

Raban et al. 16




Appendix B. (continued)

Sample 1


blue20 project-wookieBluewiki Tommy

Group affiliation web20forbiz web20forbiz 1 0.8 0.8Vuccore BlueiqBlueiq Vuccoreblueiq-ambassador blueiq-ambassadortaggingsummit2006 CioHackday Csienterprise20 HackdayTec taggingsummit2006web20summit08 web20summit08Cio Communities

Org group Lotus Lotus 1 0.8 0.9Websphere GbsSwg SalesGbs SwgDesign WebsphereSales StrategyCommunications DesignSoftware ResearchStrategy SoftwareMarketing Communications

Country Germany Germany 1 1 0.8Australia UkAustria AustriaCanada CanadaUk AustraliaEurope BrazilCemaas CemaasAsean IndiaBrazil MaltaNorway Norway

City Hamburg Hamburg 1 0.6 0.4Hursley HursleyStuttgart BerlinBerlin VancouverDresden NywHannover TorontoBoston ViennaCambridge BoeblingenLondon BostonMelbourne Nyc

Customer Ebic Ebic 1 0.4 0.3Government deutsche-bankItil SiemensSiemens ascent-captureAegon Ericssonascent-capture FoursightBasf IbvBayer Ing-dibaChevron p-dakimEads ppl

Organization Cisco cisco 1 0.4 0.7Sap appleApple xingCynefin academyalcatel-lucent cynefinCvi microsoft

(continued)

Raban et al. 17





Sample 1


Daimler CviDrupal DrupalDucati AutoidMicrosoft Daimler

Job description Architect architect 1 0.6 0.5tech-sales tech-salesManager managerConsultant Tap-innovatorExecutive presalesHcm developerw3-editor distinguished-engineerWebdesign DesignerEditor ExecutiveDesigner As-delivery

Special skill Blogger blogger 1 0.4 0.6Mentor mentorCommunitybuilder 5live-speakerInventor Communitybuilderthinkplacecatalyst BlogcentralBlogcentral Inventorportal-evangelist quickr-expertWebmaster AuthorPodcasting HackerSpeaker portal-evangelist

Hobby Photography Biker 0 0.4 0.3Writer PhotographySoccer WriterVideo FuwaClimbing WiiGaming battle-of-bandsGolf FreshJazz ArtSailing DiverSnowboard Golf

Personal adj Evangelist Innovator 0 0.4 0.7early-adopter EvangelistCompetitive early-adopterLeadership Guruportal-expert CatalystCatalyst portal-expertInnovator CompetitiveEducation CreativeInputaccel LeadershipSupport Friend

Sample 2

Popular self-tags Popular social tags ovl@1 ovl@5 ovl@10

Information Tech Java Linux 0 0.4 0.5Linux JavaArchitecture RfidGic TweUml AotRfid energy-utilities-w3-toolbar-links(1)Sonar Autoiddomino-infrastructure Standards

(continued)

Raban et al. 18





Sample 2


Server ArchitectureClient Sonar

Internet Tech web20 web20 1 0.6 0.7Secondlife secondlifeSoa Soaj2e open-sourceAjax WebWeb metaverseopen-source j2eJavascript Ajaxvirtual-worlds blogcentralPhp iphone-blog

Theme Socialnetworking Collaboration 0 0.8 0.7Collaboration WorkInnovation social-networkingsocial-computing social-computingsocial-software social-softwareSecurity user experienceuser experience InnovationKnowledgemanagement TaggingVirtualization MobileWork Communication

IBM product Portal Portal 1 0.8 0.6lotus-connection lotus-connectionNotes QuickrQuickr SametimeSametime DominoDomino Learningwebsphere-application-server OdwLearning IbmcomAgile System-zEcm Tap

External product Eclipse Mac 1 0.6 0.5Twitter EclipseIphone IphoneDogear MoleskineFacebook DogearMysql ObuntuPhotoshop MozilaFirefox AisBlackberry 2010outlook(1)Mac Twitter

Project Fringe Fringe 1 0.6 0.6Beehive Bluepediaproject zero EtsBluepedia p-vistaEts p-profiEagle Bluemailp-vista RelmapKoala EagleDmtf DmtfRexx Project-puls

Group affiliation Vuc web20forbiz 0 0.6 0.6web20forbiz research-software-strategy-meeting-2006(1)Blueiq Vucglobal-innovation-community taggingsummit2006domino-administration Blueiq

(continued)

Raban et al. 19





Sample 2


Kcblue pms-contactsit-architects web20summit08(1)web20summit08(1) Swattaggingsummit2006 web20tic(1)Swat z-community

Org group Lotus Lotus 1 0.6 0.8Websphere SwgRational GbsTivoli websphereSales RationalMarketing IsslSwg TivoliStrategy StrategyGbs SalesServices Research

Country Canada germany 0 0.4 0.7Italy AustriaSweden UKUK ItalyEurope BrazilSwitzerland USAIndia IndiaAustralia CanadaGermany SwedenHungary Europe

City new-york Hamburg 0 0.6 0.6Hamburg HursleyWimbledon New YorkBremen PhoenixPhoenix TorontoNew Jersey RomeToronto BoeblingenPittsburg WimbledonVancouver MainzMaintz Atlanta

Customer Itil Bowstreet 0 0.4 0.5Optim MaristVodafone eon-isMarist DatevGovernment VodafoneEbic GovernmentRails RailsGap CitybankEon CitygroupAvaya Philips

Organization SAP Sap 1 0.4 0.5Ibm IbmOracle CosiMicrosoft AppleUnix UsaidAdobe PubdisAcademy CiscoIssw IsstAudi IsswIsst Microsoft

Job description Architect Tech sales 0 0.6 0.8Tech sales tap-innovator

(continued)

Raban et al. 20





Sample 2


Manager ArchitectCommunitybuilder ManagerProject manager DeveloperSwita Project managerConsultant CommunitybuilderDeveloper DirectorDesigner SwitaProgrammer Designer

Special Skill Blogger Blogger 1 0.8 0.6Mentor MentorInventor InventorCoaching HackerSpeaker SpeakerPhotographer CoachingHacker BlogmasterCatalyst TechgenPresenter Mindmapperjava-performance Dogmaster

Hobby Jazz Coffe 0 0.2 0.2Photography SeaSailing WiiGolf ScubaSea GuitarSoccer SailingSquash CyclingSkiing CookGuitar ClimbingSymphony Snowboard

Personal adj Compliance Innovator 0 0.4 0.5portal-expert portal expertdomino-expert FriendsCreative LeaderPervasive CreativeCompetitive EvangelistEvangelist Partnercommunity-leader Participantarchitectural-thinking Early adopterEarly adopter Domino-expert

Raban et al. 21




Documents

Impression management through people ... - IBM Research · for social media sampling and design Daphne Ruth Raban Faculty of Management, University of Haifa, Israel ... cal and terminological