Transforming enterprise communications through the blending of social networking and unified...

◆ Transforming Enterprise CommunicationsThrough the Blending of Social Networking andUnified Communications Michael J. Burns, R. Bruce Craig, Jr., Brian D. Friedman, Peter D. Schott, and Christophe Senot

The blending of the emerging technology of social networking with unifiedcommunications has the potential to radically change how enterpriseemployees communicate. Enterprise social networking applications helpemployees build organizational collective wisdom and work more effectivelyby discovering implied relationships through shared social data. Unifiedcommunications applications provide suites of real time and non-real timecommunication enablers like click-to-call, click-to-IM, presence, and voicemail. Blending these two technologies permits text/audio/video conversationsto be treated as social objects whose data and metadata can be stored,searched, tagged, and followed in social networking applications, just likeother social objects such as people. In turn, exposing social data aboutconversations enables innovative new capabilities like recommendingconversations for someone to participate in based on inferred interests, orfinding and contacting an expert based on knowledge about conversationsin the social network. This paper describes People & Projects, an experimentalapplication used in Alcatel-Lucent to investigate advances in enterprise socialnetworking. It also outlines early work on the integration of social networkingand unified communications. © 2011 Alcatel-Lucent.

(See [6] and [7] for reviews of the literature on infor-

mation overload.)

Fortunately, two trends provide important oppor-

tunities to create breakthroughs in how people as

knowledge workers and as ordinary citizens conduct,

access, and exploit conversations in the future. One

important trend is the rise of social networking appli-

cations that make it much easier to create, maintain,

and understand social relationships. An early overview

IntroductionIn both our professional and in our personal lives,

we are continually engaged in a torrent of conversa-

tions. These conversations may take the form of voice

conversations (face-to-face or over the phone), video

conferences, email, instant messages, blogs, microblogs,

wiki updates, and so on. As technological capabilities

expand, the number of these conversations in which

we are involved seems to grow exponentially. We are

flooded with conversations and unable to keep pace.

Bell Labs Technical Journal 16(1), 19–34 (2011) © 2011 Alcatel-Lucent. Published by Wiley Periodicals, Inc. Published online in Wiley Online Library (wileyonlinelibrary.com) • DOI: 10.1002/bltj.20483

20 Bell Labs Technical Journal DOI: 10.1002/bltj

of the emergence of social networking applications

such as MySpace* and Facebook* in the consumer

space is provided by [2]. More recently, social media

applications such as blogs, microblogs, wikis, and

social networks, have been adapted for use in enter-

prises [3, 4, 5, 17, 18]. Many of these advances fall

under the general heading of “Enterprise 2.0” [13],

which is promoted as a collaboration platform for

“how work really gets done” in organizations.

The second trend is the growth of unified com-

munications (UC) capabilities. In the late 1990’s and

early 2000’s unified messaging applications began to

appear, which provided integrated access to non-real-

time communication data such as voice mail, email,

and faxes across devices (see, for example, [15]). UC

is the extension of unified messaging to include real-

time communication services such as instant messag-

ing, presence, telephony, video conferencing, and

speech recognition. An early prototype of a context-

aware UC application is described in [11]. Alcatel-

Lucent offers a portfolio of enterprise UC products

including My Instant Communicator and My

Teamwork®. One important capability of UC applica-

tions is the digitization of conversation data, which

means that conversations can be treated as content

that can be stored, indexed, and referenced. In addi-

tion, conversation data represents a rich object that

includes related metadata such as the conversation’s

date, time, duration, and set of participants.

We see huge opportunities in the blending of these

two technologies—social networking and unified com-

munications. The opportunities go well beyond the

obvious step of enabling “click to X” features from

social networking sites (where X could be call, con-

ference, or instant messaging [IM]). Rather, radical

new ways to access and manage conversations will be

enabled by treating unified communication data as

“social objects” that can be tagged, followed, and

searched in communications applications. This paper

describes our vision for how the blending of social net-

working and unified communications will profoundly

change how people communicate in the future.

Applications are emerging that do some of the

things we propose in this paper. Perhaps the applica-

tion currently available that comes closest to our vision

is TweetDeck* [19], which aggregates incoming mes-

sages from a variety of social media sources including

(at the time of this writing) Twitter*, Facebook,

LinkedIn*, FourSquare*, MySpace, and Google Buzz.

However, what we propose as the blending of social

networking and unified communications goes well

beyond what is currently available by decomposing

and recomposing communication messages to provide

super-personalized recommendations and views of

conversations for the user. Details of this approach

are provided in the sections on “Blending Social

Networking and Unified Communications,” and

“Research Challenges in Working with Conversations

as Social Objects.”

We begin with a description of our work on

People & Projects (P&P), an enterprise social net-

working application developed for use in Alcatel-

Lucent. We point out some of the social behaviors

that are enabled by this experimental application and

present results showing how P&P, and especially its

social tagging capability, has been used in Alcatel-

Lucent. We also discuss how work with P&P has fur-

thered our thinking about the application of social

behaviors to a variety of enterprise-based objects.

Next, we explore an extension of this approach of

blending social networking and unified communica-

tions conversations, present some example use cases,

and outline a high-level architecture. We then describe

some of the research challenges that arise from this

new approach, outline some of the potential business

impacts, and present our conclusions. Our hope is that

the work outlined in this paper will one day enable

enterprise knowledge workers to manage one million

conversations as easily as they manage one conversa-

tion today.

Panel 1. Abbreviations, Acronyms, and Terms

API—Application programming interfaceIM—Instant messagingP&P—People & Projects social networking

applicationSOAP—Simple Object Access ProtocolUC—Unified communications

DOI: 10.1002/bltj Bell Labs Technical Journal 21

People & Projects: A Platform for InvestigatingEnterprise Social Networking

People & Projects is an experimental social net-

working application being used across Alcatel-Lucent

for information sharing and collaboration. Aside from

providing information about all members of Alcatel-

Lucent, P&P also allows for the creation and manage-

ment of project-related data, text-based conversations,

and feedback items. P&P provides support for tagging

objects (such as people and projects in the applica-

tion), following objects, and relationship discovery.

These social behaviors are described in more detail

below. In addition, P&P contains a notification infra-

structure for sending informational “communiques” to

P&P community members about actions of interest

that occur within the application. P&P federates infor-

mation from other internal social networking appli-

cations such as Bell Labs TV (for audio/video content

sharing) and Yammer* (for microblogging). Figure 1shows an example of a P&P people profile page.

Projects, conversations, feedback, and tags also have

profile pages in P&P which provide data about

instances of those objects.

In development since mid-2008, P&P was origi-

nally intended to be an experimental research plat-

form and was initially available only within Bell Labs.

In July of 2009, P&P was made available to the entire

Alcatel-Lucent community. Since that time, several

P&P—People & Projects

Figure 1.Example P&P people profile page.


thousand Alcatel-Lucent staffers have visited P&P, and

data for nearly 700 projects has been entered. Visitors

have submitted more than 100 feedback items and have

started a number of text-based conversations. Several

organizations within Alcatel-Lucent have come to

depend on P&P and/or federate data through its vari-

ous application programming interfaces (APIs).

Social Behaviors in P&PAs a social networking application, P&P facilitates

several important social behaviors: 1) building collective

knowledge through “tagging” and “updating”; 2) com-

munication through “click-to-call,” email links, and

text-based conversations; 3) awareness through “fol-

lowing” and event notification; and 4) discovery

through search and relationship graphs. Users per-

form these social behaviors through actions on the

core abstraction objects in P&P: “people,” “projects,”

“conversations,” and “tags.” [16]. Tags are used to cap-

ture relationships the users care about between

objects. Figure 2 shows the P&P object model. Ovals

indicate the core abstraction objects in P&P, solid

arrows indicate behaviors that can be performed on

those objects, and dashed lines indicate relationships

between the objects. These methods of interacting

with the social objects are open to any member of

the P&P social network as long as they are logged-in

to P&P, so that the application can track who is taking

the actions.

For most data elements, P&P’s open contribution

policy enables any user to change any data element

and for all users to see the updated data and tags. For

example, any user can tag or update another user’s

profile data, not just data about themselves. In this

way, P&P encourages fuller participation by users and

leverages the collective knowledge across the enter-

prise to build a more complete picture of people

and projects in the company. Tagging provides a

community-based mechanism to better and more fully

categorize objects through unstructured short descrip-

tions of the core abstraction objects [8]. Because all

tags are visible to everyone, all P&P users, not just the

“tagger,” benefit when tags are applied to objects.

Click-to-call simplifies the process of initiating a

voice call to another person. Calls can be placed

directly from someone’s profile page, through com-

munication between P&P and Alcatel-Lucent’s My

Teamwork UC product. Text-based conversations in

Person

Tag

Conversation/feedback

Project

Apply/update/follow

Create/update/follow

Create/update/follow

Linked

Linked

Linked

Contains

Contains

Legend:ObjectSocial behaviorRelationship

Update/follow

Figure 2.People & Projects object model.


P&P provide a way for people to ask questions, pro-

vide status updates, and discuss subjects of interest.

Users can “follow” objects in P&P to receive notifica-

tions when actions occur that affect the object of

interest. If the object of interest is a person, they are

also notified when that person performs certain

actions using P&P. Such notifications take the form

of “communiques” that appear on the application

website and/or are sent via email to the “follower,”

according to his/her preferences. Figure 1 shows

example communiques for the logged-in user at the

top of the page.

Users can search P&P to find information of inter-

est and discover relationships through the use of the

graphical relationship navigator. For example, using

the navigator, a user can discover that another person

shares many of the same tags and/or is associated with

many of the same objects (e.g., projects or conversa-

tions). Figure 3 shows a sample relationship naviga-

tor graph for one user.

Figure 4 shows some example behaviors that can

be applied to different types of objects in P&P. One

point to note is that the behaviors of tagging, follow-

ing, and discussing can all be performed on three

Figure 3.Sample relationship navigator graph.


types of core abstraction objects in P&P—people, pro-

jects, and conversations.

P&P UsageP&P was initially deployed as an experimental

tool within Bell Labs in October 2008. Nine months

later, in July 2009, it was made available to all Alcatel-

Lucent employees. Word about P&P has spread virally,

rather than through corporate-wide announcements

or publicity. Table I provides statistical information

about P&P usage. Some key conclusions that can be

gleaned from the statistical and other data available in

P&P are detailed below.

Building collective knowledge. Collective knowl-

edge is a shared understanding of information within

a community. Tagging is the primary way that collec-

tive knowledge is built within P&P. Table I shows sev-

eral interesting points about how people use tags.

Approximately 13 percent of users who have logged

into P&P have tagged at least one object. This level of

tagging usage is similar to that of other social net-

working applications that have been deployed in

other large enterprises [14]. Eighty-six percent of tag-

gers tagged their own profile page. Slightly less than

half of the taggers also tagged at least one additional

object. The mean number of times a given tag was

used is 1.85 times, indicating that a particular tag is

most often only applied to one or two objects.

Seventy-four percent of users did whatever tagging

they did in P&P all in the course of one day. In an

effort to encourage reuse of tags and to simplify the

tagging process, a tag type-ahead feature was imple-

mented that intelligently recommends pre-existing

tags to users based on the first few characters they

enter when tagging an object.

Communication. A “click-to-call” feature, first

introduced into P&P in November 2009, enables users

to initiate a phone call between themselves and

another person in Alcatel-Lucent by clicking on an

icon from a person’s people profile page. Click-to-call

was used 131 times in the seven months between

when it was first introduced and May 2010 (the date

Soci

al b

ehav

iors

/re

lati

on

ship

ob

ject

s

Tagging

Following

People

Fabrice is tagged with“social networking”

Mary tagged Jose

Alexis and Bruce hada conversation

Brian is followingPete

Jean-Marc is beingfollowed by Frank

Core abstraction objects

Projects

Fuji project istagged with “webapp”

Rainier project isbeing followed byJose

François asked aquestion about theWhitney project

Conversations

Feedback note istagged with“usability”

Feedback note isbeing followed by Bill

Bob replied toFrançois’s question

Discussing

Figure 4.Enterprise social objects and behaviors.


of this writing). A text conversation feature, first

introduced in February 2010, enables users to create

and reply to text-based conversations on topics of

interest. Eight text conversations were created in the

four months between when it was first introduced

and May 2010. The level of usage of these two fea-

tures within P&P has been disappointing when com-

pared to other features such as tagging. Those people

who have used the P&P “click-to-call” capability do

tend to use it again periodically, but P&P text-based

conversations have not caught on. This may be

because “Yammer,” an enterprise microblogging tool,

was already in use in Alcatel-Lucent before the text-

based conversation feature was added to P&P, and it

provides similar functionality.

Awareness. As mentioned above, P&P users can

“follow” people, projects, tags, and conversations

within P&P to be notified about changes to those

objects’ data in P&P and be notified when a person

they are following takes common actions within P&P.

Table I shows that people choose to follow other peo-

ple about twice as often as they choose to follow pro-

jects. It should be noted, though, that people named

as project leaders or project members automatically

receive notifications regarding changes to their pro-

ject(s) by default. Thus, people only need to explicitly

follow a project of interest if they are not on that pro-

ject’s team. Fewer tags are followed than people, but

more tags are followed than projects. Based on a lim-

ited set of people who provided early feedback on the

ability to receive notifications from P&P via email, this

is an important feature. There is not enough usage

data yet about email notifications to draw conclusions

about how they are being used and about how much

traffic back to P&P they may be generating.

Discovery. P&P’s search mechanism is the primary

interface that people use within P&P to find informa-

tion about other people, projects, or conversations

that may be of interest to them. There were approxi-

mately 20,000 searches performed in P&P between

October 2008 and May 2010. Roughly 90 percent of

those returned at least one matching result from the

P&P database. When searching in P&P, people most

often search for other people’s names (approximately

two-thirds of the time). People also search for project

names (or parts of them) and terms for types of skills

and/or attributes of people (often in an attempt to

find an expert with that skill or attribute). The rela-

tionship navigator is a mechanism within P&P that

Table I. People & Projects general usage statistics.

People & Projects Statistic Value*

Number of unique people who have �3,500visited P&P

Number of people who have been tagged 722

Number of people who are being followed 320

Number of projects in P&P database 691

Number of projects that have been tagged 266

Number of projects that are being followed 159

Number of conversations in P&P database 8

Number of conversations that have 6been tagged

Number of conversations that are being 5followed

Number of unique tags in P&P database 3,854

Number of tag applications 6,505

Number of tags that are being followed 236

Percentage of users that have applied at 13.5%least one tag

Percentage of taggers who tagged their 86%own profile

Percentage of taggers that have tagged 42%other objects

Percentage of tags that are more than a 47%single word

Percentage of taggers that have applied 34%all their tags in one action

Percentage of taggers that have applied 74%all their tags in one day

Mean number of tag applications 15.6per tagger

Mean number of distinct tags applied 8.5per tagger

Number of objects that have been tagged 1,049

Mean number of objects tagged per tagger 3.6

Mean number of times that a given tag 1.85is used

* Values as of May 21, 2010P&P—People & Projects


helps users discover important relationships between

people, projects, and conversations through a graphi-

cal representation. It has received positive feedback

from users as an important discovery mechanism.

Enterprise Objects as Social ObjectsOne of the insights emerging from the work on

P&P is awareness of the power of “social objects.” We

define a social object as any digital object a) that can

be referenced at a known location; b) whose metadata

is exposed through services; and c) to which social

behaviors such as tagging and following can be

applied. People are an obvious example of social

objects, when considered from the standpoint of social

networking applications like Facebook, Twitter, or

P&P. P&P extended that model to treat projects,

feedback, and text-based conversations as social

objects. [16] describes how the same model can be

applied to other enterprise objects, specifically soft-

ware APIs and applications, in order to provide a flexi-

ble, community-based “application workbench” for

sharing software components and critical information

about them.

Blending Social Networking and UnifiedCommunications

The same insight about the power of social objects

can be applied to conversations, regardless of the

mode of the conversation. Voice calls, video confer-

ences, email, and instant messages have rich meta-

data associated with them such as the conversation

participants, start (or sent) date and time, duration

(in the case of voice calls and video conferences), and

subject (which might be explicit or inferred from the

content). If this metadata is exposed and is accessible

at a known location, social behaviors such as tagging

and following can be applied to instances of the con-

versation. In many cases, data created by the conver-

sation (the conversation’s “content”) can also be

referenced at a known location and exposed.

Treating conversations as social objects provides

an opportunity to blend unified communications and

social networking in an innovative way, going well

beyond simple click-to-call capabilties, for example.

We see two important ways that this blending will

provide benefits. First, leveraging social data about

conversations will enable powerful new filtering and

recommendation capabilities. First-order social rela-

tionships (such as knowing who is following a par-

ticular conversation) will be important to users, but

the real power of this model comes from using sec-

ond-order relationships to show inherent relation-

ships between multiple conversations and between

conversations and other enterprise objects. For

example, someone who has tagged the same project

that I tagged might be someone I would want to

learn about. Similarly, I might want to learn more

about someone who has replied to a conversation I

replied to, or someone who is following someone

else that I am also following. These relationships can

then be used to make inferences driving recommen-

dations and filtering decisions that are pushed to

users.

The second important benefit of blending unified

communications and social networking by treating

conversations as social objects is the ability to extract

component parts of conversations and then recom-

bine them (mash them up) in new ways that are

meaningful to users. In this way, not only are the rele-

vant conversations pushed to a user, but the compo-

nents that make up a given conversation are tailored

to the specific user’s needs based on social data about

the user and about the conversation components. This

personalized conversation mashup helps the user dis-

cover and learn new insights about collective knowl-

edge in the organization that they would not have

gained otherwise.

The VisionBy blending social networking and unified com-

munications, we envision a suite of applications and

technologies that will revolutionize people’s ability to

exploit information and content produced by the

flood of simultaneous conversations that they

encounter every day. This will be accomplished by

providing:

• A means to automatically capture and securely

store relevant information about multi-modal

conversations, including the content of the con-

versations.

• An ability to identify, extract, and expose parts or

components of conversations.


• An ability to recombine or mash-up those com-

ponents to provide personalized views of conver-

sations.

• An automatic conversation “summarizer” based

on the semantic content of a conversation.

• Dynamic intelligent recommendations and filtering

based on semantic analysis of conversations, social

relationships, presence/geolocation information,

explicit “like” actions and other recommendations

by users, and “buzz” about conversations to help

people learn which conversations may be valuable.

• Advanced user interfaces that provide people with

personalized recommendations and multiple per-

spectives on conversations.

These new applications will pull together past,

present, and future conversations for people in a way

that could not be done before and will help them

leverage their own collection of information and con-

nections to discover new connections between people

and information, and make themselves known and

valuable to others. Work is in progress in Bell Labs to

realize this vision of blended social networking and

unified communications. The following sub-sections

provide example use cases showing how these tech-

nologies can be used together. Following the use

cases, a high-level architecture is proposed to realize

this vision.

Use CasesThe following use cases provide some examples of

how the functionality envisioned for this blended

social networking and unified communications plat-

form will radically change how people conduct,

access, and manage conversations.

Find an expert. Exposing conversations as social

objects within a social networking application allows

people to use the rich metadata associated with the

conversations to help solve technical issues. Consider

a case where Bob is working on designing the foo

widget that utilizes SOAP technology and he does not

understand some aspects of SOAP. Bob visits his social

networking application and enters a query to look for

an expert on SOAP. The social networking applica-

tion recommender process queries conversation meta-

data and suggests Bruce as an expert because he has

worked on an earlier version of the foo widget and he

has had several recent discussions that reference

SOAP. In addition, Jane is leading a project, “Sazoom”

that was recently tagged with SOAP. The recom-

mender process shows both Bruce and Jane are avail-

able and suggests a conference call that it can initiate.

Contact center solution creation. In today’s envi-

ronment, contact center agents (people) must manu-

ally sort through a customer’s problem and match it to

known solutions. The agent may have access to a

knowledge base of solutions and previous customer

interactions, but this problem solving requires manual

effort by the agent. Consider a case in a future contact

center environment where a customer reaches out to

a company’s contact center through a phone call,

email, or via a web form to report a problem and the

contact sessions are handled as social objects by auto-

mated intelligent software agents. Based on the cus-

tomer’s environment and problem description, the

“contact center solution agent” (in this case, software,

not a person) reviews the conversation knowledge

base containing previous solutions and contact ses-

sions recorded in text, audio, and audio-visual for-

mat between customers and contact center agents or

support technicians. Through an intelligent relation-

ship discovery process, the automated software agent

mines the available knowledge base, conversation

data, and metadata (including tags) to find previously

successful solutions. Previous solutions might be com-

bined semi-automatically to construct a new solution

where needed, much as pre-existing web services can

be combined into new web applications. The applica-

tion would then propose the solution to the human

contact center agent or directly to the customer. The

customer applies the solution to resolve the issue and

provides feedback to the contact center so the new

solution can be cataloged.

Share conversations between multiple people.Typically, conversations between two or more people

are of interest to others who may be working on simi-

lar topics, so the ability to automatically share all or

part of the conversations with a broader group could

be a desired interaction. Consider a case where Pete

and Mike are having a conversation where the con-

versation could be written, spoken, video recorded,

or recorded in a mix of modes. They have designated


that they want their conversation to be public, so an

automated “conversation recommendation agent”

that is monitoring public conversations determines

the conversation could be of interest to Paul. The con-

versation recommendation agent notifies Paul in real

time, if his presence indicates that he is available, and

he could join the conversation at that point. If Paul is

not available, the conversation will be easily recalled

or replayed by Paul at his convenience. Treating con-

versations as social objects enables them to be well

categorized, tagged, and made easily discoverable by

interested parties. Paul or any interested party can

also annotate the conversation metadata by adding

tags or attributes as supported by the social network-

ing application. The application’s recommending of

conversations to parties (such as Paul in this example)

who are not explicitly invited by the original conver-

sation participants raises important privacy and secu-

rity questions. See the “Rights Management” section

below for further discussion about some of these

questions.

Hot topic. Consider the ability to, based on a per-

son’s social relationship graph, notify them of active

conversations related to their areas of interest that are

“hot topics” across the enterprise. For example,

because of all the recent activity related to the

Harmony project, it is one of the most active topics of

conversation this week; therefore it is added to the

“Hot Topics” list. Users who have specified interest in

topics related to Harmony or who have recently par-

ticipated in conversations related to Harmony or to

technologies related to Harmony would be notified

about Harmony being a “hot topic” and can explore

and zoom through the Harmony conversations and

can join ongoing conversations if desired. They can

also follow the propagation of this conversation topic

through the system over time.

Summarize/catch-up. Many times enterprise work-

ers will join conversations somewhere in the middle

of the conversation stream. This situation might be

caused by someone calling into a conference call that

is already half-way through, or it might be that a

worker has just discovered a conversation topic

through their social network and now wants to learn

more about the conversation. A “catch up” function to

help them quickly find out what they missed would

be of huge value. This “catch up” function would,

based on the user’s social relationship graph and per-

sonalization settings, review data and metadata from

the conversation streams to create a summary view

of the conversation that reflects the user’s interests.

The summary view could combine various conversa-

tion modalities as necessary.

High Level ArchitectureHow might a blended social networking and uni-

fied communications application be constructed to

meet the above use cases? Figure 5 shows a high

level architecture for such an application. Data from

conversations in various modes, such as voice calls,

video calls, IMs, emails, and blogs comes into the

application through the communications server(s)

Panel 2. Conversation

We define a conversation as one or more goal-oriented communications between two or morepeople that can be conducted in several modes: text (e.g., email, IM, chat), audio (e.g., phone call),or audio-visual (e.g., face-to-face meeting, video conference calls). Conversations can be short orlong in duration and can consist of one or more interactions, in one or more modes, between theparticipants. They can be digitally recorded and made available for playback or analysis. In ourresearch, we use the term “hyper-conversation” as a technical construct that refers to syntheticconversations that are decomposed and “mashed-up” from conversation data and metadata inthe application that we propose. However, for the sake of simplicity in this paper, we will use“conversation” to refer to both the real-world construct and the technical construct.


gateway. That gateway sends the conversation data

to the conversation analytics module that performs

semantic analysis and extracts metadata and the

atomic components of conversations. The semantic

analysis and component extraction are driven by

the conversation data model. After the conversation

has been decomposed, intelligent conversation rec-

ommendation agents analyze those components to

filter, synthesize, recombine, and recommend con-

versations. The output (recommendations and syn-

thesized conversations composed of multi-modal

elements) are provided to the user through the con-

versation navigator, which provides a comprehensive

user experience for managing conversations. A pow-

erful personalization model feeds the conversation

recommendation agents and the conversation navi-

gator since personalization of conversations is central

to this application. A search engine is available to

handle cases where the user wants to manually search

for conversations or their components. Matching

results from the search engine are output to the con-

versation navigator. In addition the user’s query is

logged with the personalization model to improve

personalization of future recommendations that the

application makes to the user.

Research Challenges in Working WithConversations as Social Objects

The vision outlined above requires research and

innovation in several areas to extend the current state

of the art. The sections below describe some of the

most important areas for research. The Bell Labs

Applications Research Domain recently launched a

program called “One Million Conversations” that will

address many of the research questions described

below.

Content oriented conversationinfrastructure

IM

Blog Wiki

Voice call

Email Yammer†

…

…

† Trademark of Yammer, Inc.

Conversationrecommendation

agents

Conversationsdata model

Communicationsserver(s) gateway

Conversation analytics module

Personalization model

Conversationnavigator

Search engine

IM—Instant messaging

Figure 5.High level architecture for conversations as social objects.


Collection, Identification, Representation andExtraction of Conversation Data

Each conversation includes important explicit and

implicit metadata, such as participant names, date

and time, modality, and tags. It also, of course, contains

data—the content of the conversation, and perhaps

attached files. The first step is the retrieval of conver-

sation data and metadata from disparate communica-

tions sources, a function served by the communications

server gateway. There are important identity manage-

ment questions here, since it must be assumed that

users will have different logins for different communi-

cations systems. How are user identities reconciled and

managed across these systems? Then how does the

application identify and represent the data and meta-

data associated with conversations from disparate

sources in a mode-independent way so that compo-

nents of a conversation can be extracted or subdivided

into atomic conversation elements that can be used to

derive meaning and knowledge from the conversation?

What is the right “size” for the atomic unit? For exam-

ple, is it a sentence, or a paragraph of text? Is it one

“speaker turn” in an audio or audio-visual conversa-

tion? Which metadata elements are common across

conversation modalities and which are modality-

specific?

Conversation Component Aggregation, Personalization,and Summarization

People, especially when working in a business set-

ting, typically engage in conversations to pursue some

goal (for example, to express a need or to acquire, share,

or critique information) within a given context [10].

After conversations are subdivided into their atomic

components (see above section), how then can those

atomic parts be automatically recombined into new per-

sonalized representations of the conversations? These

representations must be context-aware and goal-

oriented to help the participants of the conversation or

other people who later access the conversation to pursue

some personally meaningful goal related to the conver-

sation. For example, a person may want to be able to

“catch up” with parts of a conversation that transpired

when he was not present. Rather than listening to the

full audio of a missed conference call or reading through

the entire thread of an email discussion, it would be

helpful to be able to receive a summary of the conver-

sation. Enabling this scenario might involve a number

of technologies such as speaker identification, speech-to-

text conversion, and semantic extraction. This research

topic is closely related to the first research topic described

above. An additional challenge would be putting the

pieces together to support conversation summarization

in near real-time. While no multi-modal conversation

summarizer has been built at this time, Zhou [21]

describes an email summarizer, “ClueWordSummarizer,”

based on a fragment quotation graph approach.

Providing this conversation summarization capability

holds promise for driving major improvements in effi-

ciency for enterprise knowledge workers.

Intelligent Recommendation and Filtering EngineIntelligent recommendation and filtering of con-

versations is at the heart of the blended social net-

working and unified communications vision. There

are many interesting research questions to pursue in

this area. How can the semantic content of a conver-

sation, along with knowledge about someone’s previ-

ous conversations, skills, interests, and friends (their

personal context) be used to determine which con-

versations to recommend to him? How can someone’s

current presence and location data be added to the

mix above to determine on an almost real-time basis

which recommended conversations are high priority

versus “nice to know”? How can data about what is

happening in the social network be used to determine

which conversations are “hot topics,” and how can

that information be used to determine which people

should know about those conversations? How can

second-order social relationships be used to recom-

mend new people for someone to contact, for exam-

ple, based on shared conversational interests? How

accurately can the context of a conversation be

inferred from metadata about the conversation or

from the content of the conversation? What are the

practical or performance-related limits on intelligent

conversation recommendation when the size of the

user community and the size of the conversation

database is increasing over time?

User ExperienceThe advances that we gain from the previous

research topics all need to come together into an


innovative user experience that allows people to

view and interact with conversations in a natural

manner, which hides the complexities of the tech-

nology and displays only the information they need

but no more. The conversation navigator in Figure 5

serves as an interface for the overall user experience

that is envisioned. How can conversation compo-

nents from various modalities be combined into a

holistic, coherent view of the conversation? How can

people navigate through various layers of detail

about a conversation to easily find relevant infor-

mation? What different perspectives or orientations

on conversations need to be provided to enable peo-

ple to search, filter, and sort conversations effectively

and efficiently? How can conversations be repre-

sented to users to show their relationships to other

forms of media and content?

While the design of these user experiences is still

an ongoing research effort in Bell Labs, two user

interface constructs—faceted browsing [9, 20] and

zoomable views [1]—offer promising approaches for

this work. The combination of these constructs with

the idea of multiple perspectives allows the user to

zoom in and out of a set of conversations as defined

by the faceted filter criteria, as well as enabling them

to “flip the view” of the conversations based on a

frame of reference (geographic, time, organization,

participants, and/or goals). As the user navigates

through the various zoom levels, the user interface is

supplemented (surrounded) by pertinent informa-

tion for the level displayed. This could include arti-

facts created during the conversations and other

recommended information of interest. At the most

detailed level, a user can see the basic components of

the conversation, and, at applicable zoom levels, a

user may click to join an active conversation or anno-

tate past conversations.

Rights ManagementThe discussion thus far in this paper about expos-

ing conversation data is an oversimplification in one

important sense—it implies that everyone would

always want their conversations exposed for every-

one else to access. Of course that is not the case.

Thus, privacy and security become important ques-

tions that must be considered if this blended social

networking and unified communications vision is to

be realized.

One way to think about privacy and security in

this context is in terms of rights management. How

can the participants in a conversation be given the

power, through easy-to-use tools, to control what

conversations and parts of conversations they expose

to others? It is likely that there will need to be various

rights management levels, such as “private” where

only the conversation participants can see the con-

versation (like with email today); “semi-private”

where metadata about the conversation, but not the

full content, might be made available to others; and

“read only” where the conversation content is

exposed but copy/paste/forwarding are not allowed.

Will users need control of these rights levels on a

“conversation atom” level? What are the appropriate

default values for different types of conversations?

What happens if two conversation participants desire

different privacy levels for the same conversation?

How can these rights management options be pre-

sented to users so that they understand the options

and are not overwhelmed by the choices? How do

rights management levels affect what can be exposed

in conversation summaries, as described in the

“Conversation Component Aggregation, Personaliza-

tion, and Summarization” section above?

Creative Commons [12] provides free licenses,

consistent with the rules of copyright, and tools to

mark content so it can be shared, remixed, and/or

used commercially according to the wishes of the cre-

ator(s). Creative Commons is widely used and may

serve as a model for some aspects of rights manage-

ment in this work.

Business Opportunities Enabled by Blending SocialNetworking and Unified Communications

The work described in this paper should result in

several important business impacts. The most obvi-

ous application is a new generation of unified com-

munications products for the enterprise that enables

social behaviors to be performed on unified commu-

nications data. At the core of these UC products will

be advanced conversation personalization, filtering,

and recommendation capabilities. In addition, these


products will provide a user experience that enables

people to find, view, and manage conversations as

coherent, multi-modal aggregates, rather than as dis-

parate fragments.

The concepts described in this paper should also

lead to advances in contact center products, such as

Alcatel-Lucent’s Genesys® product line. The ability to

provide dynamic intelligent recommendations and fil-

tering based on semantic analysis of conversations,

social relationships, presence/geolocation informa-

tion, and conversation popularity will be a huge dif-

ferentiator in the contact center space, revolutionizing

contact center workflow. There are also great oppor-

tunities to take advantage of these technologies to

enable customers to do much more effective self-

support, significantly reducing customer support costs

for enterprises.

Finally, it should be possible to leverage advances

from this research program in communication termi-

nal products, such as Alcatel-Lucent’s 8000 series tele-

phone terminals. Terminal-based applications could,

for example, provide context information and/or fil-

tering and forwarding for incoming phone calls to

users, where the context information is based on

knowledge of previous conversations, social networks,

and other parameters.

ConclusionsThe blending of social networking and unified

communications to create new classes of social com-

munication applications promises to revolutionize

communications among enterprise knowledge work-

ers. People & Projects is a valuable platform for

experimenting with new social networking and com-

munications concepts in a “live” enterprise environ-

ment, while at the same time serving as an

important knowledge-sharing tool for Alcatel-Lucent

employees. One of the new concepts emerging from

P&P is the idea of treating enterprise business objects

as “social objects” to enable social behaviors such as

searching, tagging, and following to be performed

on them. This paper has described how treating con-

versations in the enterprise as social objects provides

a radically different approach to blending social net-

working and unified communications, one that goes

well beyond “click to call” in enabling important

new capabilities for users. While there are still

important research questions to address regarding

the design, architecture, and implementation of

these new social communications applications, sub-

stantial progress is being made toward the vision

described in this paper.

AcknowledgementsThe authors would like to acknowledge Bruno

Aidan, Fabrice Dantec, Yana Kane-Esrig, Paul

Labrogere, Arnaud Vergnol, Mike Wengrovitz, and

Francis Zane for stimulating discussions about the

questions of why and how to combine social net-

working and unified communications.

*TrademarksFacebook is a trademark of Facebook, Inc.Foursquare is a trademark of Foursquare Labs, Inc.LinkedIn is a trademark of LinkedIn Corporation.MySpace is a registered trademark of MySpace, Inc.TweetDeck is a trademark of TweetDeck, Inc.Twitter is a registered trademark of Twitter, Inc.Yammer is a trademark of Yammer, Inc.

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(Manuscript approved December 2010)

MICHAEL J. BURNS is a technical manager in the Intuitive Collaboration Department of theApplications Research Domain at Alcatel-Lucent Bell Labs in Murray Hill, New Jersey.He currently leads a team researchinginnovative social media applications. During

his career, he has performed systems engineering, userexperience design, and software development on awide range of operations support systems andmultimedia- and web-based applications and services.He is a member of the Association for ComputingMachinery (ACM). Dr. Burns earned a B.A. inpsychology from Washington and Lee University inLexington, Virginia, and an M.A. and Ph.D. in cognitivepsychology from the University of California, LosAngeles (UCLA).

R. BRUCE CRAIG, JR. is a distinguished member of technical staff at Alcatel-Lucent Bell Labs.Based in Alexandria, Ohio, he currently isworking in the Application ResearchDomain on enterprise social networkingstrategies and architecture. His research is

focused on application of social networking data andmetadata to new communications paradigms andsoftware development processes. Dr. Craig is a softwarearchitect and previously worked on the Alcatel-LucentEnterprise Sales Portal pilot, as well as on knowledgemanagement system research, and software platformsfor telecommunications. He is Board Certified inanatomic pathology. He received his M.D. degree fromSaint Louis University, St. Louis, Missouri, and a B.A. inchemistry from Wittenberg University, Springfield,Ohio.


BRIAN D. FRIEDMAN is a member of technical staff in the Intuitive Collaboration Department ofthe Applications Research Domain atAlcatel-Lucent Bell Labs in Murray Hill, NewJersey. He is currently a software developerfor the People & Projects experimental

social networking application and is one of a group ofresearchers working on the blending of socialnetworking and unified communications. Having spentmost of his career as a software developer andresearcher within Bell Labs, he has also performedsystem engineering, user interface design, systemadministration, and project management. Mr. Friedmanearned a B.S. in computer science/electronics from theState University of New York at Binghamton inBinghamton, New York, and a Master of Science inadvanced technology with a specialization in computerscience from the Thomas J. Watson School ofEngineering in Binghamton, New York.

PETER D. SCHOTT is a research engineer in the Intuitive Collaboration Department of theApplications Research Domain at Alcatel-Lucent Bell Labs in Murray Hill, New Jersey.He is currently a member of a teamresearching and developing social

networking based services and components that enableenterprise workers to discover information andrelationships and collaborate in innovative ways. Hisprevious assignments included systems engineering,software development, working with productdevelopment teams in leveraging emergingtechnologies and processes within their products andservices, and project management on a wide range ofapplications across Bell Labs and Alcatel-Lucent. Mr. Schott earned a B.S. in computer science fromMonmouth College.

CHRISTOPHE SENOT is a researcher in the Services Infrastructure Research Domain at Alcatel-Lucent Bell Labs in Villarceaux, France. Hegraduated from the Engineering School ofInformation Technologies and Management(EFREI) and TELECOM Paris Tech (ENST) in

Paris, France. He worked for a short time as a softwareengineer at Capgemini, a worldwide systemsintegrator, prior to joining Alcatel-Lucent. His currentresearch activities are on user and group profiling andpersonalized applications. ◆

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