SCENE: Structural Conversation Evolution NEtworkMarina Danilevsky∗, Joshua Hailpern, and Jiawei Han

danilev1@illinois.edu; jhailpe2@cs.uiuc.edu; hanj@cs.uiuc.eduDepartment of Computer Science, University of Illinois Urbana-Champaign

201 N Goodwin Ave, Urbana, IL 61801-2303, USAtel: 1-(217)-333-4428 fax: 1-(217)-244-6073

∗ contact author

Abstract—It’s not just what you say, but it is how you say it.To date, the majority of the Instant Message (IM) analysis andresearch has focused on the content of the conversation.The mainresearch question has been, ‘what do people talk about?’ focusingon topic extraction and topic modeling. While content is clearlycritical for many real-world applications, we have largely ignoredidentifying ‘how’ people communicate. Conversation structureand communication patterns provide deep insight into howconversations evolve, and how the content is shared. Motivatedby theoretical work from psychology and linguistics in the areaof conversation alignment, we introduce SCENE, an evolutionnetwork approach to extract knowledge from a conversationnetwork. We demonstrate the potential of our approach by takingthe task of matching conversation partners. We find that SCENEis more successful because, in contrast to existing approaches,SCENE treats a conversation as an evolving, rather than astatic document, and focuses on the structural elements of theconversation instead of being tied to the specific content.

I. INTRODUCTION

It’s not just what you say, but it is how you say it. Todate, the majority of the Instant Message (IM) analysis andresearch has focused on the content of the conversation.Themain research question has been, ‘what do people talk about?’focusing on topic extraction and topic modeling. While contentis clearly critical for many real-world applications, we havelargely ignored identifying ‘how’ people communicate. Con-versation structure and communication patterns provide deepinsight into how conversations evolve, and how the content isshared.

We guide our investigation through theoretical work frompsychology and linguistics on ”Communication Accommoda-tion Theory” or CAT. First formalized in 1971 by HowardGiles [7], CAT suggests that during a conversation, both thelexical and structure of our conversations change based onthe phrasing, vocabulary, and sentence structure of our con-versation partner. By analyzing these changes, researchers canuncover how people interact, why certain interactions fail, andhow to improve them in the future. While findings have a widevariety of applications [18], [16], [12], [6], its primary methodof analysis is through hand coding. Hand coding involvesresearchers going through video or written transcripts, andnoting different types of behavior and conversational patterns.These hand-coded results can then be fed into ”bag of words”software [14], which performs frequency based analysis oncategories (e.g. topic, affect, phrasing). This process is slow,

cumbersome, and the outcome of such an analysis is onlysurface level.

We hypothesize that data mining techniques can be appliedto logs of CMC conversations to explore and uncover patternsof communication. However, to explore the patterns in depth,and uncover new interactions, we must first show that thesetechniques can find known phenomena. This paper thereforefocuses on the alignment of conversation structure [15]. Inother words, two people who have a conversation togetherwill have similar structure to one another. To determine ifdata mining techniques can detect conversation alignment, wewill focus on matching conversation partners (the ability of thecomputer to find a conversation’s partner simply by examiningthe structure of their text).

Because the focus of this work is on conversation structure,rather than content, we situate this work in a set of IMconversations (between strangers) where the topic was pre-determined. We use a chat log dataset from Hailpern et al[9]which consists of conversations between 16 pairs of peopleon pre-determined topics, which allows us to explore thestructures of the conversation, rather than the content, whichis the focus of our approach.

We begin by discussing linguistic conversation analysis,which will guide our approach, in contrast to the currentapproaches to chat log analysis in the information retrieval andlinguistics communities. We introduce our dataset of chat logs,and describe the metrics used to model the ‘structure’ of a chatconversation. We then introduce SCENE, an evolution networkapproach to match conversation partners, and compare it to twoother approaches to the matching problem: Euclidean distanceand k-means clustering. In contrast to existing approaches,SCENE treats a conversation as an evolving, rather thanstatic document, and focuses on the structural elements of theconversation instead of being tied to the specific content. Weshow the results of a simple experiment on our dataset, anddiscuss the implications. We conclude by discussing the work’slimitations, its significance, and where future work might lead.

II. BACKGROUND

To situate this research, we describe the linguistic literatureon analyzing conversation structure and existing approachesto analyzing chat logs.

A. Linguistic Conversation Analysis

We inform our analysis and methods through a detailedexamination of the theoretical underpinning of conversationstructure from the literature on linguistic analysis. The specificdetails of the following linguistic theories will guide ourcomputational analysis and ground our design decisions inwell supported literature.

1) Communication Accommodation Theory: Communica-tion Accommodation Theory (CAT) was developed by HowardGiles (dissertation in 1971, published in 1973) [7]. In short,CAT attempts to explain the rationale for how and why peopleslanguage change during conversations. This could be changesin content, phrasing, vocabulary, and other speech patterns.The two most noteworthy patterns of change are:

• Convergence describes how an individual shifts hisspeech patterns in various interactions so that they moreclosely resemble the speech patterns of the conversationpartner(s)

• Divergence describes how an individual shifts his speechpatterns in various interactions so that they differ from thespeech patterns of his conversation partner(s)

CAT is a well accepted, well-researched theory that is con-sidered a major socio-psychological theory of language andsocial interaction [18]. Some current avenues of research lookacross cultures for how language changes and evolves duringinteractions [16], or how age affects interaction in business set-tings [12], or public interaction with police offers [6]. Further,CAT suggest that people will increasingly accommodate theirdialogue partners, and try to signal empathy, to elicit approval,trust, respect, or cooperation. This behavior may comprisedmatching word choices, sentence or utterance length, or eveninformation density.

2) LSM, Alignment Theory and Mutual Adaptation : Inaddition to CAT, other theoretical approaches to analyzingconversations have been developed. Linguistic style matching(LSM) refers to the degree to which two people in conversationadjust, their own speaking behavior, or style, to match theirpartners behavior. Specifically, LSM suggest that two partic-ipants engaged in conversation will utilize similar words inconversation to facilitate coordination in the conversation itself[13]. In other words, Participant 1 will speak to Participant 2thereby influencing Participant 2 who will, in turn, influenceParticipant 1. Pickering and Garrod describe the creation anduse of these common words as ”Alignment Theory.” [15]Further, research on Mutual Adaptation expands upon LSM,suggesting that changes in conversation structure often happenautomatically or subconsciously (CITE correct Cappella 1985article). Further, Cappella’s research on Mutual Adaptationstates that mutual adaptation is pervasive and that this is theessential characteristic of every interpersonal interaction[3].Therefore, we aim to show that data mining techniques canuncover this central part of interpersonal communication,which to date, has only been detectable by hand-coding.

B. Chat Log Analysis though LIWC

One of the most common methods of conversation analysisis through hand-coding. This is an arduous process in whichexaminers read a transcript of a conversation and mark allobservations of certain key phenomena. To ensure reliability,multiple independent examiners read the same transcript, andinter-rater reliability scores are calculated to determine of theobserved behavior did, reliably occur.

In 2006, a linguist named Pennebaker developed LinguisticInquiry and Word Count (LIWC) [14]. LIWC is a programthat uses a bag-of-words approach to measure content (e.g.,judgments, personality, topics, etc.) as well as the more lowlevel elements of language (e.g., punctuation, phrasing, etc).Overall LIWC is a hand/hard coded analysis of languageacross numerous dimensions. LIWC processes one, or agroup of text files, and outputs the results in each of thesedimensions, which include general categories (word count,words per sentence, etc.), linguistic categories (percentage ofwords in the text which are nouns, verbs, etc.), psychologicalconstructs (e.g., affect, cognition), personal concern categories(e.g., work, home), paralinguistic categories (e.g., assents suchas ”mmhmm”), and punctuation categories (periods, commas,etc.). While LIWC does provide a surface level analysis of aindividual conversation, it does not examine the relationshipbetween multiple conversations, rather, it only produces a setof cursory statistics about one specific transcript. Further, alldimensions and aspects of analysis are both hard-coded andhand-coded. Any previously unknown interaction/word/etc.cannot, therefore, be classified.

C. Text Mining Approaches

[CURRENTLY WORKING ON THIS SECTION, WILLBE A PARAGRAPH OR TWO]

Using some simple data mining techniques that focus onthe structural features of the chat conversations, we can un-cover aspects of interpersonal interaction and communicationpatterns without being limited by hand-coded dictionaries,or variations on topic. In particular, we focus on solving asimple matching problem: given two conversations, with twoconversation partners participating in each (for a total of 4people), and a log of each person’s side of their conversation(for a total of 4 one-sided logs), can we find each person’sconversation partner, regardless of conversation topic?

III. MOTIVATION

Most people are unaware of these subtle but importantchanges to language during interpersonal communication.However, through a detailed examination of these languagepatterns, we can uncover and quantify these interactions. Todate, literature in computer science, information retrieval anddata mining has focused on topic and content of conversation.However, there is a wealth of literature in linguistics andpsychology that suggest that the manner and conversationalpatterns of interpersonal communication is just as critical

to understand and capture. Even those in the field of lin-guistics acknowledge that we do not fully understand theconversational patterns of those using computer medicatedcommunication (e.g. IM) [15]. But through this understanding,we can directly influence and understand why communicationbreaks down, and how to improve it.

This work therefore takes a step towards answering howcomputer systems can uncover these patterns in communica-tion as postulated by CAT. By pre-determining conversationtopic, we can focus on the conversation structure and linguisticchanges that occur over time as conversation partners attemptto communicate. To validate the theory of this approach, thiswork seeks to apply data mining techniques to the vast theoret-ical literature in linguistics and communication patterns. If datamining techniques can uncover known conversational changes(which normally must be coded by hand), they may be ableto then uncover new and yet unknown conversational patterns.This work is intended as a first step in the larger process ofapplying approaches such as SCENE to chat conversation logs.

IV. DATASET, APPROACH, & METRICS

A. Why focus on function words?

When conversation partners engage, they often seek to aligntheir speech. This is central to human communication [3].One major aspect of that alignment is the synsemantic1 orgrammatical structure of sentences. This grammatical structureis defined by how content words (e.g. nouns, verbs, adjectives,etc.) are linked together by function words.

Content words are generally tasked with communicating theactual meaning of a communication. Function words, mean-while, give style and structure to the communication. Comparethe brusque ‘Broke. Send money.’ with the polite ‘I am broke;will you please send some money?’ Both sentences containthe same content words, but all the words which are presentin the polite sentence and missing in the abrupt are functionwords. In other words, content words can roughly be definedas nouns, verbs, adjectives, and adverbs; function words areevery other grammatical class of words, such as prepositions,pronouns, auxiliary verbs, conjunctions, grammatical articlesor particles [4].

The English vocabulary contains almost 100,000 Englishwords, but only about 500 function words. Function wordsare thus about 0.05% of our vocabulary, and yet they makeup about 55% of all the words we speak, hear, and read [13].They are the ‘structural’ aspect of communication.

B. Metrics

What are the useful ‘structure’ metrics to help matchconversation partners? In other media such as documents, wewould be interested in the number of words per sentence.However, chat conversation are peculiar in that people willoften eschew punctuation in favor of hitting the ‘return’ keyat the end of each thought, referred to as an ‘utterance’. In

1synsemantic(adj): of a word or phrase meaningful only when it occurs inthe company of other words

this way, utterances (each of which is one line of log text)replace sentences, and so we will note the total number ofutterances (lines), in each conversation. as well as the averagenumber of words per utterance (line). As discussed, we payadditional attention to function words. We wish to transformthe conversation into ‘function word space’ by ignoring allcontent words, and note how much of each conversation ismade up of function words. Therefore we will be interestedin the total number of function words used, the percent ofthe conversation that consists of function words (calculatedas the count of function words over the count of all words),and the relative frequency of each individual function word(calculated as the count of that word over the count of allfunction words)2.

To summarize, we will use 5 ‘structural’ metrics:• FunctionWordRatio = #function words / total #words• WordsPerLine = #function words / #lines• LineCount = total #lines (‘utterances’)• FunctionWordCount = total #function words• FunctionWordsVector = a vector of the relative fre-

quency of each individual function word.The FunctionWordsVector can be thought of as representing

the conversations position in the ‘function word space’. Eachfeature of this space is a function word, and the vector foreach conversation is relatively sparse, since in a ten minuteconversation each participant used on average only 34 distinctfunction words.

We now go into more detail regarding the dataset we used.

C. Hailpern et. al. Chat Log Dataset

To situate our investigation to detect conversation structureand match conversation partners, we utilized a set of IM logs3

generated in 2010 by Hailpern et. al. [9]. To generate this dataset, 32 participants were anonymously paired together. Eachpair would have two conversations. For each conversation,a topic for ”debate” was given, and each participant wasassigned a position (pro or against). The two debate topicswere:

• The age at which people gain the right to vote should belowered to 16 years

• Smoking should be banned in all public placesUnlike most chat log datasets, Hailpern’s has the unique

and useful feature that the conversation topics were pre-determined. This eliminates a large amount of ambiguityregarding the content. This dataset, therefore, allows us toexamine whether SCENE can uncover the structure of con-versations when there is no chance of gleaning anythinguseful from the content. Additionally, since the subjects inthe Hailpern et al experiment were strangers, debating fairlyserious topics, the conversations were less likely to be full

2It is interesting to note that we tried setting these values to (frequency offunction word) / (total count of all words), and generally saw no discernibledifference in our results.

3The full dataset will be available online in May 2011, or by request fromthe authors

of abbreviations and Internet grammar. It was therefore morelikely that our analysis would bear fruit.

D. Pre-Processing of Data

The chat logs were pre-processed to extract salient featuresfor analysis. To this end, for each side of a conversation, weperformed the following pre-processing steps:

1) Cleaned Words - we eliminated all punctuation, anddouble spaces. The resulting side of the conversationonly consists of words

2) Part of Speech - we used openNLP [5] to tag the partsof speech in each conversation.

3) Function words only - to examine the synsemanticstructure of the logs, we replace all non-function wordswith a placeholder word, ’XXX.’ From this simplifiedversion of the conversations, a dictionary of all functionwords present in dataset was created.

By the end of this pre-processing, each conversation wouldbe broken up into two files (one representing each side of theconversation). The files would be broken up with one messageper line. All content words were represented by a ’XXX’ andfunction words were left as is. Figure 1 illustrates the processthat each log file went through to be mapped into functionword spaces. In addition, a dictionary file was created thatrepresents all 224 function words that were encountered acrossall 32 conversations.

Fig. 1. Two sentences, with Parts of Speech tagged, and with function wordsidentified. This is a log from an actual experiment conversation

V. ANALYSIS & METHODS

A. Methods

In the 32 conversations we examine, 16 pairs of peopleeach had two conversations with each other, on the assignedtopics of banning smoking, or lowering the voting age. Ineffect, these can be viewed as each pair of people having oneconversation, switching topics halfway through. Therefore thisdataset consists of 16 conversations, with two participants ineach.

Consider a simple matching problem where two conversa-tion pairs are chosen at random. Each person’s log of their sideof the conversation is examined, and his most likely partneris chosen from the other three people. This is a matchingproblem with replacement, so although one person may be

correctly assigned their conversation partner, the conversationpartner in turn might be assigned one of the two non-matchingindividuals. If choosing randomly, the chance of all four peoplebeing matched with their correct conversation partner is (1/3)4

or a little bit over 1%.Each conversation is represented by a vector of 228 metrics

(the Function Words Vector comprises 224 of these, and theother 4 are FunctionWordRatio, WordsPerLine, LineCount,and FunctionWordCount as described above). For every twopairs of conversations, consisting of 4 one-sided conversationlogs, we pick up one log at a time, find the nearest logfrom the other three candidates, and assign it as the likelypartner. We perform this matching with every possible pair ofconversations from the 16 in our set; therefore we perform(16*15/2) = 120 permutations.

1) Euclidean Distance: The simplest way to calculate theclosest vector among a set of candidates is to use Euclideandistance. Therefore this is our first method. For each of the120 permutations we calculate the Euclidean distance betweenall 4 conversation vectors, and for each vector, name its closestneighbor to be the correct conversation partner. We give eachpermutation a score of 0 to 4, reflecting the number of correctmatches that occurred. On average, using Euclidean distancefound correct matches for 1.84 of the 4 conversations.

2) K-Means Clustering: Another way to match is byclustering. We use k-means with k=2 on each of the 120permutations. In this case, we need to interpret the resultsof each clustering more carefully when assigning a score toeach permutation. We illustrate with an example: Considerparticipants A, B, C, and D. A and B are conversation partners,as are C and D. If we cluster these four participants using k-means, we will see one of three outcomes:

1) Cluster1 = {A,B}, Cluster2 = {C,D}. Every conversationlooks at its fellow cluster members to find a match. Afinds B and earns a score of 1, similarly B finds A, andC and D find each other, all earning a score of 1. Thusthe total score for this permutation is 4.

2) Cluster1 = {A,C}, Cluster2 = {B,D} (where C and Dmight be interchanged, with the same result). Everyconversation looks at its fellow cluster members to finda match. A finds C, which is incorrect, and earns a scoreof 0; B, C, and D fare similarly. Thus the total score forthis permutation is 0.

3) Cluster1 = {A,B.C}, Cluster2 = {D} (or any other 3-1clustering). Every conversation looks at its fellow clustermembers to find a match. A must choose between B andC to be its match. A has a 0.5 chance of choosing B, itscorrect match, and 0.5 chance of choosing C, the wrongmatch, so the score for A would be best represented as0.5. B similarly has a 0.5 chance of picking A or C, andalso earns a score of 0.5. C has no partner since D isnot in Cluster1, and therefore C earns a score of 0. Dsimilarly is marooned by itself, and earns a score of 0.Therefore the total score for this permutation is 1.

The average success rate using k-means over the 120permutations was finding a match for 1.54 of the 4 conver-

Fig. 2. Slice of the dataset visualized as heterogeneous information network.Each rectangle node represents the log of one side of a conversation. Eachcircle node represents a function word. The weight on each edge is the relativefrequency of the Function Word in the Conversation

sations. We observed that 23% of the time k-means clusteredeveryone correctly (Outcome 1), 16% of the time k-meansfailed completed (Outcome 2), and 61% of the time k-meansreturned the case with one cluster of size 3, and one clusterof size 1 (Outcome 3).

3) SCENE: Both Euclidean distance and clustering with k-means matched fewer than 2 out of 4 conversations correctlyon average. We hypothesize this is because these approachesdo not take into consideration the evolutionary nature ofdata, when the data is conversations. We propose a sim-ple alternative approach, a Structural Conversation EvolutionNEtwork (SCENE) which builds on the theories Communica-tion Accommodation Theory and linguistic alignment. SCENEviews the conversations as a network, with nodes representingconversations connected to nodes representing function words.The weight on the edges represents the relative frequency ofeach function word in each conversation. Figure 2 illustratesa slice of the network.

An advantage to the network approach that SCENE takesis that it is easy to introduce the concept of evolution overtime. It is natural to split a conversation into several parts- for example, when the topic or the nature of a conversa-tion changes. A conversation can therefore be thought of assequence of conversation parts, each of which is treated as aseparate static node, which forms its own edges with the Func-tion Word nodes. This sequence of nodes also forms edges,explicitly mirroring the order of the original conversation.This transformation into sequences allows SCENE to easilyexamine the conversation as it evolves, rather than deal witheach conversation only as a whole, like all current linguisticapproaches do (e.g. LIWC). The limited amount of functionwords, and the sparsity of unique function word use in anygiven conversation (recall that the participants in our datasetonly employed about 34 unique function words on averageover two ten-minute conversations), means that transforminga conversation into a sequence of conversations requires very

Fig. 3. Two conversations, each shown as a sequence of two parts. Thefirst halves of the conversations are shown on the left, the second halves areshown on the right.

little additional space.Figure 3 illustrates a slice of how two conversations in our

dataset might look in the network that SCENE examines. Sinceall participants participated in essentially one twenty-minuteconversation with a change of topic in the middle, it is naturalto split each conversation into a sequence of two conversations.It is possible that people change their conversation style whenchanging topics, because one or both of the participants aremore/less interested, or more/less informed on the new topic.However, this approach actually supports our findings evenfurther, that the trend is not based solely on the content ofthe conversation, since even when users switched topics, theystill sought to align with each other. Additionally, it was theonly natural splitting point to choose given our dataset - itwould have been difficult to support splitting the conversationsinto, e.g., 3-minute chunks (having no good answer to theimmediate question of why 3 minutes would be chosen, ratherthan 2 or 5).

An advantage of the network framework is that it doesnot dictate that all conversations must be split into an equalnumber of sequences - the nature of the data should guidethe splitting process. The particular dataset used in this papercreates a simple network structure, with each conversationsplit into a sequence of two conversation parts, and eachparticipant conversing with exactly one other participant; butwe envision this framework to be increasingly useful as longerconversations are added to the network, and participants haveadditional conversations and change conversation partners.

Within our dataset, SCENE calculates the difference of eachmetric for the first and second parts of each conversation, anduses that delta as the representative metric for the conversationas a whole. Therefore the metrics SCENE uses are indicativeof the evolution of the conversation, and, as it turns out,are better suited for matching conversation partners. For aconversation consisting of a sequence of Conv1 and Conv2,SCENE calculates the five structural metrics discussed aboveas follows:

• FunctionWordRatio, WordsPerLine, LineCount,FunctionWordCount: each metric is calculated as thedifference between the metric for Conv2 and the metricfor Conv1 (note that we do not use absolute value, topreserve the trend of the change)

• FunctionWordsVector is calculated as the Euclideandistance between the FunctionWordsVector for Conv2and the FunctionWordsVector for Conv1 (again, in thatorder, to preserve the trend of the change)

With these five ‘delta’ structural metrics for each conver-sation, we again go through each of the 120 permutations,of picking up two random pairs of conversations and tryingto find a match for each of the four conversation logs. Tocalculate the closest conversation from a set of candidateswe tried two variations, both driven by the hypothesis thatconversations which evolve in a structurally similar mannerare a good match:

1) Find the Euclidean distance between the ’delta’ struc-tural metrics between the conversation we are trying tomatch, and every other possible conversation log; choosethe logs with the minimum distance as the match

2) For each of the five ’delta’ structural metrics of the con-versation we are trying to match, order the other possibleconversation logs from closest to furthest, resulting infive ordered lists. Employ an ensemble voting methodwherein for each list, award the closest conversation 1point, the second closest two points, and so on. Theconversation with the lowest accumulation of points isthen chosen as the winner.

The second approach, which gives equal weight to each ofthe five metrics proved to be the slightly more successful inpractice, and it is the one we employ and report.

VI. RESULTS & DISCUSSION

For the matching problem, of pairing four conversationpartners together correctly, SCENE proved more adept thanEuclidean Distance or K-Means. Although we do not go intoa comparison with LIWC4 or another such similar hand-coded approach, we hold that part of the SCENE paradigmis automatically extracting a significant amount of knowledgefrom conversations without ever knowing the topic or contentof anything that was analyzed.

Table I presents the matching scores for each algorithm, asdiscussed in the previous section, illustrating that SCENE issignificantly better at matching conversation partners. Table IIgoes into additional detail for each algorithm, allowing us tosee the distribution of scores which contributed to the meanscore for each approach. Of the 120 pairs of conversationsexamined by each algorithm, we note what percentage ofthe permutations were awarded a score of 0, 1, 2, 3, or 4,representing the number of conversations whose matches werecorrectly identified. Recall that because of the difference incalculating the score of the k-means algorithm, it can onlyaward each permutation a score of 0, 1, or 4.

4not in the least because the software is not freely available

TABLE IMEAN SCORES OF MATCHED CONVERSATIONS

Mean Scores Main DatasetEuclidean 1.84K-Means 1.54SCENE 2.28

TABLE IIDISTRIBUTION OF MATCHED CONVERSATION SCORES

Main Dataset 0 1 2 3 4Euclidean 28% 8% 28% 23% 13%K-Means 16% 61% n/a n/a 23%SCENE 6% 13% 46% 18% 17%

Euclidean Distance does fairly well, although in over aquarter of the cases (28%) it gives a score of 0, by identifyingthe wrong partner for all four of the conversations. Contrastthat SCENE, which completely failed to find any matchesin only 6% of the cases, suggesting that it is acting moreintelligently and nearly always finding at least one correctmatch. Additionally, while Euclidean has roughly the samesuccess with finding matches for 0, 2, or 3 conversations,SCENE is by far the most likely to find matches for two ofthe four conversations. Anecdotal observation of the algorithmsuggests that this occurs when one pair of conversations iswell aligned, and both partners find each other, whereas theother pair of conversations is badly aligned, and neither partnerfinds the other. Again, this suggests that there is support forSCENE’s approach, but more exploration is needed to betterunderstand which metrics are most informative.

Finally, we notice that k-means clusters 3 conversationstogether, apart from the fourth conversation, more than 60%of the time. This suggests that straightforward clustering maynot be the most productive approach to solving this type ofmatching problem, because clustering presupposes, in a way,that items mirror each other, and that if A is likely to withB, then B is equally likely to cluster with A. However, inthe case of conversation alignment, it would be incorrect tosay that person A always aligns with her conversation partner,person B, in the same way and to the same degree that personB in turn aligns with her. The similarity is intuitively toostrong a requirement, and we see it proven in practice, ask-means is the worst performing of our compared approaches.It is worth considering that with a larger dataset, consistingof longer conversations between many more than four people,this drawback to the k-means approach may be exacerbated.

A. Limitations and Future Work

The chat logs from the Hailpern et. al. experiment [9] weresmall in both number and length. Two ten-minute conversa-tions is not a long time in which to adjust to another person’sspeech, and it is encouraging to see alignment occurring inthis small time period. The next step is to obtain more chatlogs to see if how well our approach generalizes to largersequences of longer conversations. This would also allow usto build on our analysis which is currently limited to matchingup 4 participants, due to the noisiness of the short conversation

logs. Additionally, the current dataset is artificially constructedso that every participant has exactly one conversation partner.A more complex dataset where each person has multipleconversation partners would be a logical next step.

There are clearly important inherent differences betweensynchronous, text-based computer mediated interactions andface-to-face interactions. In face-to- face interactions, theparticipants are in close proximity to one another while incomputer-mediated communication participants are physicallydistributed from one another. Nonverbal cues are also absentin a text-based computer-mediated setting. Also, the linguisticstyle of a chat conversation is different from a face-to-faceconversation. When chatting over the computer, people tendto be less formal, use more slang and abbreviation, and payless attention to grammar. It is possible that the fact that theconversation partners were strangers, as well as the ‘debateteam’ nature of the assigned topics (lowering the voting age,and banning smoking) biased the participants to a more formalstyle of communicating. It would be interesting to test therobustness of our approach if the participants are friendsdiscussing a casual topic, such as getting together over theweekend.

VII. CONCLUSION

In this work, we introduce SCENE, an evolution networkapproach to extract knowledge from a conversation network.Our approach is motivated by theoretical work from psychol-ogy and linguistics in the area of conversation alignment. Wedemonstrate the potential of our approach by taking the taskof matching conversation partners. We find that SCENE ismore successful because, in contrast to existing approaches,SCENE treats a conversation as an evolving, rather than astatic document, and focuses on the structural elements of theconversation instead of being tied to the specific content.

ACKNOWLEDGMENT

The authors would like to thank Dr. ChengXiang Zhai, Dr.Karrie Karahalios, Dr. Gary Dell, and Zhenhui Li for theirhelpful feedback.

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