Buzz: Telling Compelling Stories

Sara H. Owsley, Kristian J. Hammond, David A. Shamma, Sanjay Sood

Intelligent Information LaboratoryNorthwestern University

2133 Sheridan Road, Room 3-320Evanston, Illinois 60208

+1 (847) 467-6924

{sowsley, hammond, ayman, sood}@cs.northwestern.edu

ABSTRACTThis paper describes a digital theater installation calledBuzz. Buzzconsists of virtual actors who express the collective voice gener-ated by weblogs (blogs). These actors find compelling stories fromblogs and perform them. In this paper, we explore what it meansfor a story to be compelling and describe a set of techniques for re-trieving compelling stories. We also outline an architecture for highlevel direction of a performance using Adaptive Retrieval Charts(ARCs), allowing a director-level of interaction with the perfor-mance system. Our overall goal in this work is to build a model ofhuman behavior on a new foundation of query formation, informa-tion retrieval and filtering.

Categories and Subject DescriptorsJ.5 [Arts and Humanities]: Arts, fine and performing; H.3.3 [In-formation Search and Retrieval]: Information filtering

General TermsHuman Factors

KeywordsNetwork Arts, Emotion, Blogs, Media Arts, Culture, World WideWeb, Software Agents, Story Generation

1. INTRODUCTIONBuzzis a multimedia installation that exposes the buzz generated

by blogs. Buzzfinds the weblogs (blogs) which are compelling;those where someone is laying their feelings on the table, expos-ing a dream or a nightmare that they had, making a confession orapology to a close friend, or regretting an argument that they hadwith their mother or spouse. It embodies the author (blogger) withvirtual actors who externalize these monologues by reading themaloud. The focal point of the installation displays the most emo-

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Figure 1: An installation of Buzz in the Ford Engineering De-sign Center at Northwestern University.

tional and evocative words from the monologue, shown as fallingtext.

As an example of aBuzzperformance, Table 1 shows three sto-ries read in aBuzzperformance. The actors contribute to the perfor-mance by reading these discovered stories (found in blogs) aloud,in turn. The actors are attentive to each other by turning to facethe actor currently speaking. The central screen (shown up close inFigure 2), displays the emotionally evocative words extracted fromthe current story being performed.

To find compelling stories, Buzz mines the blogosphere (the col-lection of all blogs as a community), collecting blogs where theauthor describes an emotionally compelling situation: a dream, anightmare, a fight, an apology, a confession, etc. After retriev-ing these blogs, Buzz performs affective classification to focus onblogs with a heightened emotional state. Other techniques includ-ing syntax filtering and colloquial filtering are used to ensure re-trieval of appropriate content for the performance. After passingthrough these filters, the resulting story selections are compellingand emotional.

Several techniques are used to give Buzz a realistic feel and tomake performances engaging to an audience. Dramatic ARCs areused to provide a higher level control of the performance, similar tothat of a director. The actors are attentive to one another, turning toface the actor currently speaking. Gender classification is used to

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Figure 2: A close up view of the central screen of an installa-tion of Buzz. The screen displays emotionally amplified wordsextracted from the blog currently being performed by one ofthe virtual actors.

ensure that gender-specific stories are performed by virtual actorsof the appropriate gender. A model of speech emphasis is employedto enhance the cadence and prosody of text to speech technology.

The adoption of blogs by millions of users has resulted in muchmore than the mere presence of millions of online journals, and hascreated a new kind of communication [15]. We can expose and givevoice to such communication in installations likeBuzz. This workis part of a greater effort in an area called “Network Arts” [24],which uses information found in the world, via the network, to cre-ate artistic installations.

2. RELATED WORKOwsley, et al. [18], created an installation called theAssociation

Engine, composed of a troupe of virtual improvisational actors. Atroupe of five actors, with animated faces [22] and voice genera-tion [16], began a performance by taking a single word or phrasesuggestion from the audience, through keyboard input. They usedthis word as a seed to an improvisational warm-up game called thePattern Game, where the actors free associate to create a collectivecontext, getting themselves on the same contextual page.

Following this warm-up game, the actors would generate a OneWord Story, from the context of the warm-up. A One Word Story isa common game in improvisational theater where actors each con-tribute one word at a time to create a collective story. See Table 2for a sample pattern game and generated One Word Story from theAssociation Engine.

Using a template-based approach, theAssociation Enginewasable to generate stories that were coherent, but did not engage theaudience, as seen from the sample One Word Story in Table 2. Theylacked in character development and a general purpose.

Looking at the stories generated by theAssociation Engine, it isclear that the system faced problems that prevailed from previousyears of Artificial Intelligence research in story generation. Tale-Spin [14] used a world simulation model and planning approachfor story generation. To generate stories, TaleSpin triggered one ofthe characters with a goal and used natural language generation tonarrate the plan for reaching that goal. The stories were simplisticin their content (using a limited amount of encoded knowledge) aswell as their natural language generation.

Klein’s Automatic Novel Writer [8] uses a similar approach in

Table 1: Three stories discovered byBuzz.

I have a confession – beneath my cynical, sarcas-tic facade beats a heart of pure mush. Before yousnort milk through your nose, think about it – de-spite Connie’s best efforts, my favorite movie in theworld is THE SOUND OF MUSIC. What’s not to like?Great songs, fabulous scenery, incorrigible children, acharming nun/governess and a stern, handsome frozen-hearted captain who slowly melts under the spell of thesongs, the scenery, his kids and Julie Andrews. WhenI start that movie and the mountain scenery comes onthe scene with the birds twittering and the first chordsof music play ... I’m in heaven.

Ever sense I got into a fight with my dad I have startedto drink beer. Friday night I stole 5 beers from my dadand saturday night i stole about 5 and last night I onlystole 1. I feel as if alcohol is the only thing that canhelp me. I feel like its the only thing there for me. Idont know whats wrong with me. I dont know why Ifeel like this. I think another reason why I am so upsetis because I never get to talk to Billy one on one. Weare never alone. I am making him stay at my house thisweekend. I need to spend some alone time with him.And if that means Saturday night then that means noRachael.

Last night for instance, I dreamed that we were havingthe rehearsal dinner at an aquarium for some reason thisaquarium had a killer whale and I was dumb enough todip my feet in the tank. Well, it attacked, and in thedream I was clearly bummed out due to having a majorfoot surgery instead of a wedding. There was also adebacle with a scorpion that I won’t go into. And alsothe cake melted.

Table 2: Discovered Word Chain and One Word Story from theAssociation Engine

Pattern Gamemusic → fine art→ art→ creation

→ creative→ inspiration→ brainchild→ product→ production→ magazine→ newspaper→ issue→ exit→ outlet→ out

One Word StoryAn artist named Colleen called her friend Alicia.

Colleen wanted to go to the production at the musichall. Colleen and Alicia met up at the music hall. Totheir surprise there was no production at the music hall.Instead the women decided to go to the stage.

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order to produce murder novels from a set of locations, characters,and motivating personality qualities. The stories follow a planningsystem’s output as the characters searched for clues. The systemdoes not seem to capture the qualities of a good murder story, in-cluding plot twists, foreshadowing, and erroneous clues.

Dehn’s Author system [4] was driven by the need for an “au-thor view” to tell stories, as opposed to the “character view” foundin world simulation models. Dehn’s explanation was that “the au-thor’s purpose is to make up a good story” whereas the “character’spurpose is to have things go well for himself and for those he caresabout” [4].

In general, previous story generation systems faced a trade-offbetween a scalable system, and one that can generate coherent sto-ries. Besides Dehn’s Author, previous research in this area has em-ployed a weak model of the aesthetic constraints of story telling.

In response to the shortcomings of story generation, we chose toexplore story discovery. We found an incredible corpus of existingstories of people’s life experiences. These stories exist within asubset of blogs [11, 6] found on the Internet. We then used what welearned from other systems doing story generation to inform storydiscovery. We define a stronger model for the aesthetic elements ofstory telling and use that to drive retrieval of stories, and to filterand evaluate results.

Artistically, story telling and online communication have beenexternalized within several installations. Of the more well-known,Listening Post[7] exposes content from thousands of chat roomsand other online forums through an audio and visual display. Ina very real sense,Listening Postexposes the real-time ‘buzz’ ofchat rooms on the web. Similar toListening Post, Buzzexternalizesonline communication, through a context refined via search and ex-traction. WhileListening Postdemonstrates online communicationas a whole,Buzzfocuses on singular voices from the blogosphere,grounded in current popular topics.

Mateas’sTerminal Time[13] also tells stories extracted fromreal world sources. Storytelling inTerminal Timeis produced bytraversing a common sense knowledge base, a verified informationsource, steering a narrative arc by audience applause [12].Buzzfol-lows a dramatic arc, though at the level of the control of a directorthrough web-based, unverified, information.

3. COMPELLING STORIESA first pass at buildingBuzzrevealed that the content of blogs is

incredibly wide-ranging, but unfortunately often very dull.Buzzsucceeded in finding stories that were on point to any providedtopic, but the results were not compelling.

We found that people blogged about topics including their classschedule, what they are eating for lunch, how to install a wirelessrouter, what they wore today, and a list of their 45 favorite ice creamflavors. While this was interesting to observe from a sociologicalpoint of view, it did not make for a compelling performance. Notonly were the blogs on these topics boring, but the lengths of thestories varied widely from one sentence to pages upon pages.

We needed to give the system strategies for finding stories thatwere compelling and engaging to an audience. To do so, we definea simple model for the aesthetic qualities of a compelling story.These qualities include but are not limited to:

1. an interesting topic

2. emotionally charged

3. complete and of a length that holds the audience’s attention

4. content at the right level of familiarity to an audience

5. involving dramatic situations

6. comprised of developed characters

We designedBuzzto find stories with all of these qualities.

3.1 Topics Of InterestA compelling story is generally about a compelling topic, one

that interests the audience. For this reason, we chose the day’smost popular searches from Yahoo (provided by Yahoo buzz [27])as topics. Search engines recently started providing a log of theirmost frequently used query topics. This feed worked well as a seedto story discovery, as we were using the topics that people weresearching for most and discovering people’s thoughts and opinionson these topics.

We found Wikipedia [26] to be another source for topics of in-terest as the site maintains a list of “controversial topics”. The listshows topics that are in “edit wars” on Wikipedia as contributorsare unable to agree on the subject matter. This list includes topicssuch as apartheid, overpopulation, ozone depletion, and censorship.These topics, by their nature, are topics that people are passionateabout.

Using these two sites as sources for topics, finding compellingstories began with a simple web search restricted to the domain ofwww.livejournal.com [11], a popular blog hosting site, with eachfocal topic as a search query. Out of the first 100 results for eachtopic, about 60 tend to be actual blog entries and not blogger’s pro-file pages (this differs greatly per topic).

After discarding profile pages, the remaining blog entries are an-alyzed phrasally, eliminating posts that do not contain at least oneof the two word phrases (non-stopwords) from the topic. For ex-ample, given a topic of ‘Star Wars: Revenge of the Sith,’ entriesthat contained the phrase ‘star wars’ were acceptable, but not en-tries that merely had the word ‘star’ or ‘wars.’ The remaining blogentries were known to be relevant to the current popular topic.

After realizing the limiting results from searching merely forblogs from Live Journal, we moved to finding blogs using GoogleBlog Search [6]. This move involved creating a generalized algo-rithm for finding the blog text from a blog entry in any format (aswe previously knew the format of all blogs hosted on Live Jour-nal). We found these results to be more wide-ranging and varyingin type.

Using topics of interest as the source of topic keywords and blogsas the target, we were able to discover what was being said aboutwhat people were most interested in.

3.2 Filtering Retrieval by AffectGiven that our initial version ofBuzzwas reading blogs that were

boring, and since such a large volume of blogs exist on the web,we strove to filter the retrieved blog entries by affect, giving usthe ability to portray the strongest affective stories. Beyond purelyshowing the most affective stories, we also wanted to be able tojuxtapose happy stories on a topic with angry or fearful stories ona topic.

To build such a tool, we used a combination of case-based rea-soning and machine learning approaches [19, 25]. We created acase base of 106,000 product reviews labeled with a star rating be-tween one and five (one being negative and five being positive).We omitted reviews with a score of three as those were seen asneutral. We built a Naıve Bayes statistical representation of thesedocuments, separating them into positive (four or five stars) andnegative (one or two stars).

Given a target document, the system creates an “affect query” asa representation of the document. The query is created by selecting

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the words with the greatest statistical variance between positive andnegative documents in the Naıve Bayes model. The system usesthis query to retrieve “affectively similar” documents from the casebase. The labels from the retrieved documents are used to derivean affect score between -2 and 2 for the target document. This toolwas found to be 73.39% accurate.

For Buzz, blogs which scored from -1 to 1 were seen as neu-tral and not good candidates for a performance. When using theemotional filtering tool,Buzzwas considerably more compelling.The actors were also able to retrieve stories from the Web based onemotional stance, enabling the theatrical agents to juxtapose posi-tive and negative stories on the same topic.

Future work on this classification tool includes creating a modelaffect based on Ekman’s six emotion model (happiness, sadness,anger, disgust, fear, surprise) [10, 17, 5]. This would allow forgreater control of the flow of the performance through emotionalstates.

3.3 Filtering Retrieval by SyntaxIn our first pass at retrieving stories from blogs, we noticed that

we often found lists or surveys instead of text in paragraph form.For example, one blogger posted an exhaustive list of lip balm fla-vors. Others posted answers to a survey about themselves (theirfavorite vacation spot, favorite color, favorite band and actor, etc.).These are clearly not good candidates for stories to be presented ina performance.

To solve this problem, we chose to filter the retrieved blog entriesby syntax. Blog entries that met any of the following criteria wereremoved:

1. too many newline characters (more than six in a entry of fourhundred characters)

2. too many commas (more than three in a sentence)

3. too many numbers (more than one number in a sentence)

This method successfully filtered blog entries that contained alist or survey of some sort. While the precision of such removal ofblogs based on syntax was lower, we optimized for recall so thatall potential lists and surveys were removed for the corpus. Giventhe large volume of blogs on the web updated every minute, lettingsome potentially good blogs fall through the cracks sufficed for ourpurposes.

3.4 Colloquial FilteringShamma, et al. [24], began exploring the use of Csikszentmiha-

lyi Flow State [3] as a method of keeping the audience engagedthrough audiovisual interaction. InBuzz, for an audience to stayengaged, they must understand the content of the stories that theyare hearing. That is, the story can’t involve topics that the audienceis unfamiliar with or contain jargon particular to some field. Thestory must be colloquial. The story must also not be too familiar asthey audience could get bored.

To determine how colloquial a story is, we built a classifier thatmakes use of page frequencies on the web. For each word in thestory, we look at the number of pages in which this word appears onthe web, a frequency that is obtained through a simple web search.Applying Zipf’s Law [28], we can determine how colloquial eachword is [23]. A story is then classified to be as colloquial as the lan-guage used in it. Given a set of possible stories, colloquial thresh-olds (high and low) are generated dynamically based on the distri-bution of scores.

3.5 Dramatic SituationsThrough experiencingBuzzin the world and watching audiences

reactions and responses to stories, we discovered more generalizedtraits of compelling stories. The most compelling stories to watchwere those where someone is laying their feelings on the table, ex-posing a dream or a nightmare that they had, making a confessionor apology to a close friend, or regretting an argument that they hadwith their mother or spouse.

Codifying these qualities, we built our story discovery engine toseek out these types of stories. While still making use of multipleretrieval filters described in the previous section, we added a com-ponent to the retrieval that found stories that began with a cue thatthe writer was about to describe a dream, nightmare, fight, apology,confession, or any other emotionally fraught situation. Such cuesinclude phrases such as “I had a dream last night,” “I must confess,”“I had a terrible fight,” “I feel awful,” “I’m so happy that,” and “I’mso sorry.”

This realization was an important turning point in our system’scapabilities with regard to retrieving compelling stories. The new-est instance ofBuzzno longer focuses on the popular or contentioustopics, but instead focuses on stories in different types of emotion-laden situations (dreams, fights, confessions, etc.).

These stories are more interesting as the blogger isn’t talkingabout a popular product on the market, or ranting about a movie;they are relaying a personal experience from their life, which typ-ically makes them emotionally charged. The experiences they de-scribe are often frightening, funny, touching, or surprising. Theydescribe situations which have a common element in all of ourlives [20], giving the audience a way to relate to the content andlive through the experiences of the writer, whereas the topicallybased approach excluded the portion of the audience that was notfamiliar with the topic at hand (a popular actress, story in the news,etc.).

Including dramatic situations as a filter and search parameter notonly gets us to more interesting story topics and content, but wealso tend to see more character depth and development in the sto-ries. As writers describe dramatic situations in their lives, morepieces of their personality and personal issues with themselves andothers around them are revealed as a result.

3.6 Complete PassagesGiven the blog entries that remained after passing through the

five above mentioned filters (relevance, affect, syntax, colloquialand dramatic situations), the system must choose which pieces ofblog entries to present to the audience. This involves finding com-plete thoughts or stories of a length that can keep the audience en-gaged.

For the most part, we found that blog authors format their entriesin a way such that each paragraph contains one distinct thought.Given this, the paragraph where the dramatic situation is mentionedwith the greatest frequency will suffice as a complete story for oursystem. If this paragraph is of an ideal length (between a minimumand maximum threshold), which we determined by viewingBuzzwith stories at many different lengths, then it is posted as a candi-date story. For our system, we found that stories between 150 and400 characters long were ideal. Again, given the large volume ofblogs on the web, letting many blogs fall through the cracks be-cause they are too long or too short is fine for our purposes.

An example of three stories discovered byBuzzcan be seen inTable 1. The stories shown were retrieved and passed through allabove mentioned filters. Notice the differing emotional stances ofthe first and second stories. This was a deliberate and automaticjuxtaposition of positive and negative passages.

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Figure 3: Results of a study where participants judged howinteresting stories chosen by and rejected byBuzzwere.

3.7 EvaluationTo evaluate the effectiveness of our filters in finding compelling

stories, we conducted a user study including twelve participants.Each participant was given five stories to score on a scale fromone to ten (uninteresting to interesting). The stories were chosen atrandom from a set of stories selected byBuzzas good candidates fora performance, and a set of stories retrieved byBuzzbut removedas they did not pass one of the five filters.

On a scale of one to ten (uninteresting to interesting), the studyparticipants foundBuzzselected stories to be an average 7.13 andBuzzrejected stories to be an average of 4.3. A graph of the fre-quencies of participant scores acrossBuzzaccepted andBuzzre-jected stories can be seen in Figure 3.

4. CREATING A PERFORMANCEWhile finding compelling stories is an important aspect ofBuzz,

conveying them to an audience in an engaging way is just as cru-cial. We found several aspects of the presentation to be critical. Theperformance must follow a dramatic arc that keeps the audience en-gaged. Text-to-speech technology and graphics must be believableand evocative. Gender-specific stories must be presented by virtualactors of the appropriate gender. While these issues are a subsetof those critical to an engaging performance, we chose to addressthese directly as we feel that our findings can generalize to otherperformance systems.

4.1 The DisplayThe currentBuzzinstallations include five flat panel monitors in

the shape of an ’x’. The four outer monitors display actors rep-resented by different adaptations of the graphics from Ken Per-lin’s Responsive Face technology [22]. These faces are synchro-nized with voice generation technology [16] controlled through theMicrosoft Speech API, matching mouth positions on the faces toviseme events, lip position cues output by the MSAPI. Within thisconfiguration, the actors are able to read stories and turn to face theactor currently speaking.

The central screen (shown in figure 2) displays emotionally evo-cative words, pulled from the text currently being spoken, fallingin constant motion. These words are extracted using the emotionclassification technology described in the section on “Filtering Re-trieval by Affect.” The most emotional words are extracted by find-ing the words with the largest disparity between positive and nega-tive probabilities in the Naıve Bayes statistical model.

Figure 4: An architecture diagram of the Buzzsystem.

We’ve found this display to be a good addition to the actors as itgives the audience more context in the performance and amplifiesthe impact of the emotional words.

4.2 Director Level ControlGiven the above classifiers and filters, we are able to retrieve a

set of compelling stories. These filters and classifiers also give usa level of control of the performance similar to that of a director.Having information about each story such as its “emotional pointof view”, and its “familiarity”, we can plan out the structure of theperformance from a high level view before retrieving the perfor-mance content, giving the performance a flow, based not only oncontent, but on emotion, familiarity, on-point vs. tangential, etc.Given a topic, we can juxtapose stories with different emotionalstances, different levels of familiarity, and on-point vs. off-point.These affordances give a meaningful structure to the performance.

To provide a high level control of the performance, we createdan architecture for driving the retrieval of performance content.The structures, called Adaptive Retrieval Charts (or ARCs), pro-vide high level instructions to theBuzzengine as to what is needed,where to find it, how to find it, how to evaluate it, how to modifyqueries if needed and how to adapt the results to fit the current goalset. To get an idea of how the ARCs interact with the blog searchand filters, see Figure 4.

An example of an ARC used inBuzzis shown in Figure 5. ThepicturedARC defines a point/counterpoint/dream interaction be-tween agents. The three modules define three different informa-tion needs, as well as the sources for retrieval to fulfill these needs.The first module specifies that we want a blog entry that is on pointto a specified topic, has passed through the syntax and colloquialfilters, and is generally happy on the topic. The module specifiesusing Google Blog Search [6] as a source. The source node spec-ifies to form queries by single words as well as phrases related tothe topic. If too few results are returned from this source, we havespecified that queries are to be continually modified by lexical ex-pansion and stemming.

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Figure 5: A sample ARC from the Buzzsystem, defining a point/counter point interaction between agents.

The ARC extensible framework allows for interactions from di-rectors with no knowledge of the underlying system. In a futuresystem, we will accomplish this via a range of possible interfacesfrom storyboarding and affect manipulation to a natural languageinterface.

4.3 Compelling SpeechWhile text-to-speech systems have made great strides in improv-

ing believability of generated speech, these systems are not per-fect [1]. Their focus has been on telephony systems, where thelength of time of spoken speech is limited. In watching aBuzzper-formance, we found that the voices tended to drone monotonouslyduring stories longer than one to two sentences. An additionalproblem we encountered using text-to-speech systems to read blogswas caused by the stream of consciousness nature of some blogs,resulting in casual formatting with poor or limited punctuation.Text-to-speech systems rely on punctuation to provide natural paus-es in the speech. In blogs where limited punctuation was present,we found that the voices tended to drone on even more.

In response to these issues, we created a model for speech em-phasis. In recent work, others have created models for how to em-phasize words [21] and which words to emphasize. While thesemodels are successful, we strove to create a simple model thatwould scale to our needs. To select words to emphasize, we firstused emotional word extraction, using the Naıve Bayes statisticalmodel discussed in the section on “Filtering Retrieval by Affect”to find the words with the largest disparity between positive andnegative probabilities.

As we were using the Microsoft Speech API to control the Neo-Speech voices, we were able to use XML markup provided by theMSAPI to control the volume, rate and pitch of the voices, as wellas insert pauses of different periods (specified in milliseconds) inthe speech. Using emotional words for emphasis, we found the toptwo emotional words from each sentence. We emphasized thesewords by increasing the volume of the voice (from 70% to 100%)and slowing the rate (from an absolute rate of 0 to a rate of -2) while speaking these words. While this method did break themonotony of the speech, we found that it did not preserve the flowof the speech, resulting in choppy sounding speech. This also didnot solve the more prevalent problem of the limited punctuation ofblogs.

Table 3: Precision and Recall Scores for detection of gender-specific stories.

Document Type Precision Recallfemale-specific 92.59% 86.21%male-specific 100% 84.62%gender-neutral 89.66% 96.30%

overall 91.67% 91.67%

To smooth the choppiness of this emphasis, we found that em-phasizing the entire noun phrase where emotional words appearedtended to sound smoother than just emphasizing the emotional worditself. To accomplish this, we used a part of speech tagger [2], ex-tracting all noun phrases from a passage. We chose to emphasizethe most emotional noun phrase in each sentence. To solve theproblem of limited punctuation, we chose to insert a pause follow-ing each emphasized noun phrase, serving as a natural breakingpoint.

While our model of speech emphasis is simplistic, we’ve found itto be effective in enhancing theBuzzexperience. We expect to fur-ther tweak our emphasis model in response to audience feedback.

4.4 Detecting Gender-Specific StoriesOne problem encountered in a first pass of buildingBuzzwas

that gender-specific stories were occasionally read by actors of theincorrect gender. For example, if a blog author describes their ex-periences during pregnancy, it is awkward to have this story per-formed by a male actor. Conversely, if a blogger talks about theirday at work as a steward, having this read by a female could alsobe slightly distracting.

As a solution to this problem, we sought to detect and classifygender-specific stories. Unlike previous gender classification sys-tems [9], it was not necessary for our system to classify all stories aseither male or female. Rather, it was only important for us to detectstories where the author’s gender is evident, thus classifying storiesas male, female, neutral (in the case where gender-specificity is notevident in the passage), or ambiguous (in the case where both maleand female indicators are present).

To do this, we look for specific indicators that the story is written

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by a male or a female. These indicators include roles (family andjobs), relationships, and physical states.

To detect self-referential roles in a blog, the system looks for‘I’ references including “I am”, “I was”, “I’m”, “being”, and “asa.” These phrases indicate gender-specificity if they are followedwithin five words (if none of these five words are pronouns) by afemale-only or male-only role such as wife, mother, groom, aunt,waitress, mailman, sister, etc. Such roles were collected from var-ious sources and enumerated as such. Excluding extra pronounsbetween the self reference and the role eliminates false positivessuch as “I was close to his girlfriend.”

To detect physical states that carry gender connotations, the sys-tem again looks for ‘I’ references, as above, followed within fivewords by a gender-specific physical state such as pregnant. As indetecting roles, we also ignore cases with extraneous pronouns be-tween the ‘I’ reference and the physical state. This eliminates falsepositives such as “I was amazed by her pregnancy.”

To detect male or female-only relationships, the system looks foruse of the word ‘my’ followed within five words by a male or fe-male only relationship such as husband, ex-girlfriend, etc. Again,cases with extraneous pronouns are ignored to eliminate false pos-itives such as “my feelings towards his girlfriend.” In this our firstpass at a gender specific story classification system, we make theassumption of heterosexual relationships, which we hope to relaxin a future system.

If any of these cases exist and they agree on a male/female classi-fication, then it is classified as such. If they disagree, it is classifiedas ‘ambiguous.’ If no indicators exist, it is classified as ‘neutral.’

This gender detection tool was evaluated using a corpus of 96stories retrieved byBuzz. These stories were retrieved from anindexed corpus of stories found byBuzz. They were selected byqueries for words that often indicate gender-specificity (‘pregnant’,‘mom’, ‘mother’, ‘dad’, ‘father’, ‘girlfriend’, ‘boyfriend’, ‘hus-band’, ‘wife’, and ‘daughter’). They were sorted into three groups,stories written by females, males, or neutral (written by males orfemales). This sorting was based on textual cues that gave a clearindication of gender, and was verified unanimously by from 5 par-ticipants.

While our gender-classification system is still simple, it doesan admirable job. Results showed that the gender detection toolperformed very well, as seen in the precision and recall scoresin Table 3. Overall precision and recall were both approximately91.67%. EnablingBuzzwith the ability to detect and handle gender-specific stories has created a more realistic performance, withoutthe distraction of an actor performing a gender-mismatched story.

5. BUZZ IN THE WORLDEnablingBuzzwith the ability to discover compelling stories on

a popular topic has produced great results.Buzzhas changed froman installation that was unbearably dull, exposing the boring natureof many blogs, to a system that engages its viewers. The perfor-mance is now not driven simply by the relevance of on-line content,but by the blogger’s emotional state. The highly emotional contentengages the audience and creates a high visibility installation.

Buzzwas exhibited last year at the Athenaeum Theater as a partof the 8th Annual Chicago Improv Festival. It was well-receivedby actors, writers, producers and theater-goers alike during this tenday installation.

Buzzwas installed in the lobby of Chicago’s Second City theaterat 1616 N. Wells St. in Chicago on August 24th, 2005 for a longterm installation, currently still running.Buzzwill also be exhib-ited at Wired NextFest in New York City from September 29th toOctober 1st, 2006.

6. CONCLUSIONInitially, story discovery withinBuzzwas based on popular top-

ics. As we approached the task of engaging the user, it becamemore important that the stories themselves were compelling, as op-posed to topical. Using filters and information retrieval strategiesthat focused on finding the interesting and not the topical has re-sulted in an engaging theatrical installation. In the future, we willturn our focus back to topics, discovered within the scope of inter-esting stories.

While finding compelling stories to present is a very importantpart of theBuzzperformance, presenting these stories in a way thatis meaningful and engaging is equally important. We found issuesof gender-specificity, voice prosody, and presentation order to bethe aspects of aBuzzperformance with which we could make greatstrides in improving. Future work in the presentation ofBuzzwillinclude more realistic looking avatars and continued work on en-hancing the voice prosody.

7. FUTURE WORKOur current and future work in this area involves expandingBuzz

into a full length improvisational performance on stage, interactingwith human actors. We are building a full body projected avatarhost with voice generation, and voice recognition to take audiencesuggestions and interact with human actors. Understanding thestate of current technology in voice recognition, we are enablingthe host to drive her conversations with actors and the audience, torecover from mistakes, and express and expose her shortcomings.

This production will make use of the ARC architecture to allowa high level control of the flow of the performance. Our research instory discovery will serve as a platform for character developmentfor the host, as she can relate to and participate in discussions bytelling stories discovered from blogs related to the current conver-sation topic or audience suggestion.

8. ACKNOWLEDGMENTSThis material is based upon work supported by the National Sci-

ence Foundation under Grant No. 0535231.

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