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Designing social conversational spaces:enhancing communicative and emotional impact
 

Designing social conversational spaces:enhancing communicative and emotional impact

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A relevant portion of the information available on the Web is nowadays produced by users interactions on Web 2.0 and social network applications. In particular, many conversations take place around ...

A relevant portion of the information available on the Web is nowadays produced by users interactions on Web 2.0 and social network applications. In particular, many conversations take place around user generated contents and shared media (e.g. comments on blog posts or about virally shared links, photos and videos, product reviews, etc.). These interactions may enrich and complement the information items around which they revolve, being an highly potential source of knowledge, news and opinions, which can be mined to extract and highlight interesting information.

The presented research work is aimed at identifying and investigating the features of social interactions which can be relevant for communicative purposes, in order to provide users with a better sense making of the themes, quality and interestingness of a conversation, as well as of the authoritativeness and degree of involvement of its participants.\\
I propose a set of design strategies for \begin{inparaenum} [(a)]
\item mapping the relevant features of conversations to the metadata extracted by standard text and opinion mining tools and
\item visually representing conversations in order to ensure at a glance understanding of themes and participants involved, as well as deeper investigation (for analysis and retrieval of past conversations).
\end{inparaenum}

I introduce and discuss some metrics for estimating the relevance of messages and discussions with respect to themes, the popularity and authoritativeness of users and the density of conversations.\\
A timeline view is also proposed to visualize the length and density if conversations (represented by coloured lines). Such a visualization is complemented by coloured tag clouds summarizing the most discussed themes and the overall users sentiment towards them.

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    Designing social conversational spaces:enhancing communicative and emotional impact Designing social conversational spaces:enhancing communicative and emotional impact Presentation Transcript

    • Politecnico di Milano Department of Electronics, Information and Bioengineering Designing social conversational spaces: enhancing communicative and emotional impact Luigi Spagnolo spagnolo@elet.polimi.it November 12, 2012L. Spagnolo Social conversational spaces 1 / 51
    • Outline Conversational spaces Background The user experience: scenarios of usage Proposed model Proposed visualization Conclusions and future workL. Spagnolo Social conversational spaces 2 / 51
    • Conversational spacesWith the advent of social media • a relevant portion of the information available on the Web is product of users interactions. • many conversations take place around user generated contents and shared media comments on blog posts or about virally shared links, photos and videos, product reviews... may enrich and complement the discussed itemsL. Spagnolo Social conversational spaces 3 / 51
    • Conversational spaces | Potential Discussions as potential source of • knowledge: e.g. blogs in specialized communities of practice (programming, technology, cuisine, etc.) • news: e.g. Twitter coverage of natural disasters or other extraordinary events • opinions on people, places, products, etc. They can be mined to extract and highlight interesting information.L. Spagnolo Social conversational spaces 4 / 51
    • Conversational spaces | Open questions • What does mean that the information contained in a given conversation is interesting or relevant? For which users? In which context? • Which measurable features of social discussions should be considered? • What are the best strategies for recommending or highlighting the most interesting contributions? • What are the best strategies for exploring on-going and past discussions? • How to handle large social spaces? Many users with different background and expertise (e.g in education and cultural heritage) An high volume of messages exchanged (blogs with thousands of comments per postL. Spagnolo Social conversational spaces 5 / 51
    • Conversational spaces | My research work Communication-oriented approach: investigating possible relevant features for providing users with a better sense making of • the themes of the discussion • the degree of quality and interestingness of a conversation • the authoritativeness and degree of involvement of its participants. Towards a framework of design strategies for • determining the relevant features of discussions from metadata extracted using standard text and opinion mining tools • visual representation of past and ongoing conversations, supporting at a glance understanding of themes and participants involved deeper investigation, analysis and retrievalL. Spagnolo Social conversational spaces 6 / 51
    • Outline Conversational spaces Background The user experience: scenarios of usage Proposed model Proposed visualization Conclusions and future workL. Spagnolo Social conversational spaces 7 / 51
    • Background | Conversations: distinctive features Discussions are different from from other structured texts (articles, reviews, etc.) • joint activities involving participants with different points of views, contrasting opinions and goals (Clark, 1996). • involve collaborative (or competitive) brainstorming (Thomas, 2005) → knowledge evolves by trial-and-error, in an unplanned way. • informative content mixed with “noisy” content (e.g. phatic messages: Jakobson, 1960) necessary to handle the discussion and socialize (André et al., 2011, Makice, 2009). • can be volatile and lose currency (Prestipino et al., 2007, Schwabe and Prestipino, 2005). interest in conversations on recent or upcoming events rapidly decays after they are pastL. Spagnolo Social conversational spaces 8 / 51
    • Background | Some insights from current research (1) Interestingness of messages and conversations may depend on: • Topic relevance with respect to user interests expressed in terms of a query (Magnani and Montesi, 2010) inferred from their past messages and interactions (Chen et al., 2011) • Timeliness or currency • Density of messages • Thread length (too short → less interesting, too long → difficult to be followed) • popularity and influence of participants Tie-strength: close friends vs. acquaintances (Chen et al., 2011) Metrics for esteeming influence on Twitter (Bakshy et al., 2011, Barbagallo et al., 2012, Cha et al., 2010, Romero et al., 2011, Suh et al., 2010) Not just a matter of having many of followers (Cha et al., 2010)L. Spagnolo Social conversational spaces 9 / 51
    • Background | Some insights from current research (2) Conversation interestingness may also depend on: • Type of shared media (Bruni et al., 2012) → messages with images, videos or links have an higher impact • Polarity or sentiment of the content(Barbagallo et al., 2012, Naveed et al., 2011) → negatively-oriented tweets propagate more and mite quickly The specific context and users’ goals and attitudes are extremely relevant (Chen et al., 2011): • users more interested in news → topic relevance is important, regardless of who is speaking • users interested in building and maintaining social ties → messages from close friends are important, regardless of the topicL. Spagnolo Social conversational spaces 10 / 51
    • Background | The role of text and opinion mining Extracting the themes of the conversations • in case #hashtags are not used or are not enough • Keywords in text (e.g. Chen et al., 2011) • Named entities: people, places, events, organizations (e.g. Liu et al., 2011, Michelson and Macskassy, 2010, Nadeau and Sekine, 2007, Spangler et al., 2006) Extracting positive or negative sentiment at level of • Whole text (e.g. Pang et al., 2002, Turney, 2002) • Sentences (e.g. Kim and Hovy, 2004, Wiebe and Riloff, 2005) • Topic: keyword or named entity (e.g. Hu and Liu, 2004)L. Spagnolo Social conversational spaces 11 / 51
    • Outline Conversational spaces Background The user experience: scenarios of usage Proposed model Proposed visualization Conclusions and future workL. Spagnolo Social conversational spaces 12 / 51
    • The user experience (1)(Sc1) Browsing current conversations Goal(s): understanding what is going on, possibly getting engaged in the discussion(s) on a intellectual or emotional basis. Users: prospective participants of conversations, looking for stimulating contributions and/or at building and maintaining social ties.(Sc2) Monitoring a conversation Goal(s): checking possible updates and replies. Users: participants already involved(Sc3) Analysing ongoing conversations Goal(s): capturing emerging trends and news about a specific topic or domain Users: scholars, journalists, marketing professionals, etc.L. Spagnolo Social conversational spaces 13 / 51
    • The user experience | (2)(Sc4) Browsing past discussions in which the user(s) did not participate Goal(s): not getting involved in the conversation (the topic is now “cold”) but finding specific knowledge on a given issue. Users: novice or occasional users, lurkers(Sc5) Browsing past conversions the users were directly involved in Goal(s): retrieving specific information, recalling past interactions with friends or acquaintances Users: participants already involved(Sc6) Analysing past conversations Goal(s): investigating trends and phenomena in interactions. Similar to Sc3 Users: scholars, journalists, etc.L. Spagnolo Social conversational spaces 14 / 51
    • Outline Conversational spaces Background The user experience: scenarios of usage Proposed model Proposed visualization Conclusions and future workL. Spagnolo Social conversational spaces 15 / 51
    • Proposed model | Basic elements (1) Definition (Message) Let U be a set of users. A message sent by an user u ∈ U to a set of recipients R ⊆ U is a quintuple m = (txt, ts, u, R, snt), where • txt = text(m) is the text of the message • ts = ts(m) is the timestamp at which the message was sent • snt = sent(m) ∈ [−1, 1] is the sentiment associated to the message. sent(m) ≥ 0 → positive option, while sent(m) < 0 → a negative opinion. • the sender of m is denoted by the function snd(m), while the set of recipients of m is denoted by the function rec(m).L. Spagnolo Social conversational spaces 16 / 51
    • Proposed model | Basic elements (2) Definition (Conversation) A conversation is an ordered sequence of messages, and can be r modeled as a triple C = M, ≤, ← , where: − • M = Msg(C) is the set of messages that constitute the conversation C. • ≤ is a transitive, antisymmetric and reflexive relationship establishing a chronological order among messages in M according to their timestamp, i.e. m1 ≤ m2 ⇔ ts(m1 ) ≤ ts(m2 ) (1) r • ← is a (transitive) partial order relationship establishing a − hierarchy between a message and its replies.L. Spagnolo Social conversational spaces 17 / 51
    • Proposed model | Basic elements (3) r Notation C = m0 ← m1 , m2 , . . . , mk − • m0 (head) message staring the conversation C • m1 , m2 , . . . , mk (body): replies to m0 E.g. hierarchies of conversations and sub-conversations: r r C = m0 ← (m1 ← m1−1 , m1−2 ), m2 , m3 − − r where (m1 ← m1−1 , m1−2 ) is a sub-conversation −L. Spagnolo Social conversational spaces 18 / 51
    • Proposed model | User popularity (1) User popularity: the extent at which contributions of a specific author are likely to rise others’ interest (i.e. worth reading and deserving a reply/retweet) • (Explicit) authoritativess: (universally) recognized status as qualifying expert and/or an authoritative individual in the community. as explicit “superuser” role(s) in terms of “likes” received, number of followers, explicit rating, etc. • (Implicit) influence: perceived activity and ability to “get noticed”. The influence of u may depend on: u’s own participation in the discussions others members’ participation raised by u’s contributions. the popularity of users is higher when they are able to catch the attention of other popular usersL. Spagnolo Social conversational spaces 19 / 51
    • Proposed model | User popularity (2) Influence of user x: the number of people that are likely to notice a message posted by x infl(x) = pread (y, x)(1 + preply · infl(y)) (2) y∈f ollowers(x) • pread (x, y) = RMR(x) : probability that y reads x’s messages SM R(y) SM R(x) sent messages rate: messages sent by x per unit of time RM R(y) received message rate: messages received by y in the same unit of time. For a user u is RM R(u) = f ∈f ollowing(u) SM R(f ) • preply (constant): probability of y posting a reply/retweeting • Recursive: When a follower y replies to x, y gives extra visibility to x’s messages. The additional visibility for x depends on the influence of y.L. Spagnolo Social conversational spaces 20 / 51
    • Proposed model | User popularity (3) Broadcast case:the influence measure becomes infl(x) = pread (x) · (|U| + preply · ·infl(y)) (3) y∈U Overall popularity for user x: pop(x) = wauth · authnorm (x) + winf l · inflnorm (x) (4) • authnorm (x): normalized authoritativeness function (strictly depending on the specific context). • inflnorm (x): normalized influence function • wauth , winf l ∈ [0, 1] are the weights for the two components.L. Spagnolo Social conversational spaces 21 / 51
    • Proposed model | Theme relevance (1) Definition (Theme vocabulary) A pair (T, ) where: • T = {t1 , t2 , . . . , tn }: concepts or terms that describe conversations (the themes). • : subsumption relation, connecting narrower concepts (i.e. hyponyms) to broader terms (i.e. hypernyms) → taxonomy of concepts • Direct descendants of a term tp : children(tp ) = tc ∈ T | tc tp ∈ T, t ∈ T, tc t tp • Terminal concepts: themes without further specialization (leafs of the hierarchy): terminal(t) ⇔ children(t) = ∅L. Spagnolo Social conversational spaces 22 / 51
    • Proposed model | Theme relevance (2) Themes can possibly be handled according to a faceted classification model (e.g. see Sacco, 2009, Tzitzikas, 2009) • concepts belong to different set of categories (theme vocabularies) depending on the property/facet they refer to • Possible facets: people, locations, events or organizations mentioned in comments Notation for faceted concepts: property : “value” • e.g. keyword “technology” (extracted from messages): keyword : “technology” • named entity instance: entityT ype : “entityName” (e.g. person : “Steve Jobs” or company : “Google”)L. Spagnolo Social conversational spaces 23 / 51
    • Proposed model | Theme relevance (3) Definition (Message theme description) Theme description for message m: Themes(m) = {(ti , wi , snti ) | ti ∈ T, wi ∈ [0, 1], snti ∈ [−1, 1]} (5) • ti terminal concept relevant for the message m • wi = rel(m, ti ) a (weight): degree of relevance of ti w.r.t. m. • snti = sent(m, ti ): sentiment in m towards ti • If t is not relevant at all w.r.t. m → rel(m, t) = 0. Degree of relevance for non-terminal concepts (recursive): rel(m, tp ) = max rel(m, tc ) (6) tc ∈children(tp )L. Spagnolo Social conversational spaces 24 / 51
    • Proposed model | Theme relevance (4) Example of message theme description A user posts a review m, mainly concerning the Apple Ipad Mini tablet, with slightly negative opinions. In the message, she also incidentally mentions the Google Nexus 7 (with a fairly positive sentiment towards this second product) and Apple iCloud (with a rather strong positive opinion towards this service). Possible description for such a message: Themes(m) = { (product : “Apple Ipad Mini”, rel = 0.8, snt = −0.35) (product : “Google Nexus 7”, rel = 0.2, snt = 0.4) (service : “Apple iCloud”, rel = 0.1, snt = 0.89) }L. Spagnolo Social conversational spaces 25 / 51
    • Proposed model | Theme relevance (5) Two approaches for computing relevance w.t.r. user interest in themes: • Ranked matching The query: a vector representing users’ interests T q = τq,0 τq,1 · · · τq,n Messages ranked according to a relevance score, computed as distance/similarity w.r.t. to the query (e.g. cosine similarity) • Exact matching A query q is any of the following: a restriction R(t, wmin ) with t ∈ T and wmin ∈ [0, 1], meaning that the relevance for the term t for the target message must lay in the interval [wmin , 1]. conjunction qa and qb or disjunction qa or qb of two sub-queries qa and qb . the negation of a sub-query qc , i.e. not (qc ). Messages retrieved: those that match the filters in q (i.e. the extension of the query)L. Spagnolo Social conversational spaces 26 / 51
    • Proposed model | Interestingness (1) Interestingness of a single message: • Theme relevance • Popularity of the sender • Shared media content (videos, images, links) associated to the message. • Message length (controversial: it striongly depends on the context) • Negative sentiment associated (not taken into account: not a good design idea to explicitly suggest/highlight negative messages!) Overall conversation interestingness: • The sum of the interestingness scores of its messages • Density of the conversation (how messages are distributed in time: peak of messages exhanged → higher interestingnessL. Spagnolo Social conversational spaces 27 / 51
    • Proposed model | Interestingness (2) Definition (Message interestingness score) Overall interestingness score for message m: ξ(m, q) =wth · sim(m, q) + wpop · pop(snd(m))+ (7) +wcontent · ξcontent (m) + wlength · ξlength (m) • sim(m, q) theme relevance (similarity w.r.t. to query q) • pop(snd(m)): popularity of m’s sender. • ξcontent (m) ∈ [0, 1]: utility function associated to the shared media content in m. • ξlength (m) ∈ [0, 1]: utility function associated to the length of the message m. • wth , wpop , wcontent , wlength ∈ [0, 1]: weights.L. Spagnolo Social conversational spaces 28 / 51
    • Proposed model | Interestingness (3) Definition (Mean and variance of time between messages) Mean time between messages (MTBM) and variance of time between messages (VTBM): average and st. deviation of intervals of time between messages. 1 MTBM(C) = ts(mi ) − ts(mi−1 ) (8) |Msg(C)| − 1 mi ∈Msg(C) 1 VTBM(C) = · (9) |Msg(C)| − 1 · (ts(mi ) − ts(mi−1 ) − MTBM(C))2 mi ∈Msg(C) • ts(mi ) − ts(mi−1 ): interval of time between a message ts(mi−1 ) and the next message ts(mi ) of a conversation C.L. Spagnolo Social conversational spaces 29 / 51
    • Proposed model | Interestingness (4) Definition (Conversation density function) The (normalized) density of a conversation C in the set of conversation C: MCV(C) · |Msg(C)| denC (C) = 2 (10) C ∈C (MCV(C ) · |Msg(C )|) • |Msg(C)| number of messages in C. VTBM(C) • MCV(C) = MTBM(C) : coefficient of variation (deviation from regularity of time intervals between messages) • Short (low MTBM(C)) and highly variable (high MTBM(C)) intervals → higher coefficient of variation → higher density • New reply added → higher densityL. Spagnolo Social conversational spaces 30 / 51
    • Proposed model | Interestingness (5) Definition (Conversation interestingness score) Overall interestingness score for a conversation C: 1 ξ(C, q) = wden · den(C) + wams · ξ(m, q) (11) |Msg(C)| m∈Msg(C) • ξ(m, q) interesting score for message m (w.r.t. query q) • wden , wams ∈ [0, 1]: weights.L. Spagnolo Social conversational spaces 31 / 51
    • Proposed model | Sentiment (1) Overall sentiment score for conversation C: sum of messages sentiments values sent(C) = sent(m) (12) m∈Msg(C) Mean and variance 1 µsent (C) = · sent(C) (13) |Msg(C)| 2 1 σsent (C) = (µsent (C) − sent(m))2 (14) |Msg(C)| m∈Msg(C) Messages with very different polarities (e.g. some very negative, other very positive) : • Sum of scores (sent(C)) may suggest overall neutrality • Actually there’s lack of consensus/strong disputation → high varianceL. Spagnolo Social conversational spaces 32 / 51
    • Proposed model | Sentiment (2) Overall sentiment score for theme t, w.r.t. the generic set of messages M: sent(M, t) = sent(m, t) (15) m∈M • sent(m, t) ∈ [−1, 1]: sentiment in a message m towards t • sent(m, t) = 0: if the sentiment of m towards t is neutral, or if t is not relevant for m (rel(m, t) = 0). Mean and variance of sentiment towards t: 1 µsent (M, t) = · sent(M, t) (16) |M| 2 1 σsent (M, t) = (µsent (M, t) − sent(m, t))2 (17) |M| m∈ML. Spagnolo Social conversational spaces 33 / 51
    • Proposed model | Sentiment (3) Sentiment score for theme t, w.r.t. a set of messages M: sent(M, t) = sent(m, t) (18) m∈M • sent(m, t) ∈ [−1, 1]: sentiment in a message m towards t • sent(m, t) = 0: if the sentiment of m towards t is neutral, or if t is not relevant for m (rel(m, t) = 0). Overall relevance of a theme t in M: sent(M, t) = sent(m, t) (19) m∈M 1 Mean and variance: µsent (M, t) = · sent(M, t) |M| 2 1 2 σsent (M, t) = |M| m∈M (µsent (M, t) − sent(m, t))L. Spagnolo Social conversational spaces 34 / 51
    • Outline Conversational spaces Background The user experience: scenarios of usage Proposed model Proposed visualization Conclusions and future workL. Spagnolo Social conversational spaces 35 / 51
    • Proposed visualization | The idea Timeline view as main visualization strategy • For conveying a better understanding of conversations dynamics. on each “conversation line” • Multiple conversations on the same timeline → monitoring and comparing conversations occurring in parallel, at the same time. • Conversation flows represented as time spans on the timeline • Density of messages can be shown by representing specific messages and sub-threads as dots Two main levels of detail: • aggregate view: a summary of conversations occurring in the specified period of time • detailed view: shows at a lower of granularity the flow of messages and the density of each exchange.L. Spagnolo Social conversational spaces 36 / 51
    • Proposed visualization | Example of interface (1) adrenaline junkies amazing feat america ballon baumgartner big deal chuck yeager fox news freefall freefall record giant leap nasa paul ryan Felix Baumgartner Breaks Speed red bull roller coasters sound barrier space program world records Of Sound read The Story A Top participants B Top disccussed themes C All Keywords People Organizations Places america paul ryan sound barrier america giant leap space program baumgartner chuck yeager sound barrier USAF The headline is wrong. He :-( didnt break the sound barrier. Hes actually the first person to travel faster than D Bob74 311 Fans the speed of light. I saw it on MSNBC. 00:16 PM on October 15 2012 Full conversation (27 replies)baumgartner big deal red bull sound barrier baumgartner fox news freefall speed of light sound barrier 22h00 23h00 00h00 01h00 02h00 October 14 October 15 October 16 October 17 L. Spagnolo Social conversational spaces 37 / 51
    • Proposed visualization | Example of interface (2) adrenaline junkies amazing feat america ballon baumgartner big deal chuck yeager fox news freefall freefall record giant leap nasa paul ryan Felix Baumgartner Breaks Speed red bull roller coasters sound barrier space program world records Of Sound read The Story A Top participants B Top disccussed themes C All Keywords People Organizations Places america paul ryan sound barrier america giant leap space program baumgartner chuck yeager sound barrier USAF The headline is wrong. He :-( didnt break the sound barrier. Hes actually the first A D Bob74 311 Fans person to travel faster than the speed of light. I saw it on MSNBC. 00:16 PM on October 15 2012 Full conversation (27 replies) Thebaumgartner big deal red bull sound barrier baumgartner fox news freefall speed of light sound barrier information item (Huffington Post article) around 22h00 23h00 00h00 01h00 02h00 which discussionsOctober 14 October 15 October 16 October 17 revolve L. Spagnolo Social conversational spaces 37 / 51
    • Proposed visualization | Example of interface (3) adrenaline junkies amazing feat america ballon baumgartner big deal chuck yeager fox news freefall freefall record giant leap nasa paul ryan Felix Baumgartner Breaks Speed red bull roller coasters sound barrier space program world records Of Sound read The Story A Top participants B Top disccussed themes C All Keywords People Organizations Places america paul ryan sound barrier america giant leap space program baumgartner chuck yeager sound barrier USAF B The headline is wrong. He :-( didnt break the sound barrier. Hes actually the first person to travel faster than The most D Bob74 311 Fans the speed of light. I saw it on MSNBC. 00:16 PM on October 15 2012 active Full conversation (27 replies)baumgartner big deal red bull sound barrier baumgartner fox news freefall speed of light sound barrier participants (size is proportional to degree of 22h00 involvement) 00h00 23h00 01h00 02h00 • Image size: October 14 degree of 15 October October 16 October 17 involvement L. Spagnolo Social conversational spaces 37 / 51
    • Proposed visualization | Example of interface (4) adrenaline junkies amazing feat america ballon baumgartner big deal chuck yeager fox news freefall freefall record giant leap nasa paul ryan Felix Baumgartner Breaks Speed red bull roller coasters sound barrier space program world records Of Sound read The Story A Top participants B Top disccussed themes C All Keywords People Organizations Places america paul ryan sound barrier america giant leap space program baumgartner chuck yeager sound barrier USAF The headline is wrong. He :-( didnt break the sound D Bob74 311 Fans barrier. Hes actually the first person to travel faster than the speed of light. I saw it on MSNBC. C 00:16 PM on October 15 2012 Coloured tag barrier Full conversation (27 replies)baumgartner big deal red bull sound barrier baumgartner fox news freefall speed of light sound cloud with most discussed themes • Can be 22h00 23h00 00h00 filtered 01h00 by 02h00 type October 14 October 15 • Color: overall October 16 October 17 sentiment L. Spagnolo Social conversational spaces 37 / 51
    • Proposed visualization | Example of interface (5) adrenaline junkies amazing feat america ballon baumgartner big deal chuck yeager fox news freefall freefall record giant leap nasa paul ryan Felix Baumgartner Breaks Speed red bull roller coasters sound barrier space program world records Of Sound read The Story A Top participants B Top disccussed themes C DAll Keywords People Organizations Places Timeline america paul ryan sound barrier america giant leap space program baumgartner chuck yeager sound barrier USAF • Discussion The headline is wrong. He flows as :-( didnt break the sound barrier. Hes actually the first person to travel faster than D Bob74 311 Fans the speed of light. I saw it on MSNBC. arrows 00:16 PM on October 15 2012baumgartner big deal red bull sound barrier Full conversation (27 replies) baumgartner fox news freefall speed of light sound barrier • For each conversation: main threads (circles), participants, themes 22h00 23h00 00h00 01h00 02h00 • Callouts: providing October 14 October 15 October 16 October 17 preview L. Spagnolo Social conversational spaces 37 / 51
    • Proposed visualization | Example of interface (6) baumgartner bbc documentary fox news freefall MSNBC networks speed of light sound barrier tv show Felix Baumgartner Breaks Speed Of Sound read The Story Top participants Top disccussed themes for this conversation All Keywords People Organizations Places baumgartner fox news freefall speed of light sound barrier Bob74 311 Fans The headline is wrong. He didnt break the sound barrier. Hes :-) actually the first person to travel faster than the speed of light. I saw it on MSNBC. 00:16 PM on October 15 2012 Alice84 251 Fans NOT MSNBC, probably Fox News since they get more B :-( things wrong than any other network. bbc documentary fox news tv show 00:19 PM on October 15 2012 freefall Chuck 112 Fans :-| He speed of light is 186,000 mi/per sec. It was the speed of sound. A 00:21 PM on October 15 2012 Dean 342 Fans BBC is going to put a documentary next month about :-| the jump, if anyone cares? fox news 00:32 PM on October 15 2012 Eleanor66 12 Fans Knowing the BBC it will be biased and show :-( incorrect or incomplete facts. 00:34 PM on October 15 2012 Dean 342 Fans Oh, please. :-| 22h00 23h00 00:35 PM on October 15 2012 00h00 01h00 02h00 Alice84 251 Fans :-) Youre thinking of Fox News :-). 00:21 PM on October 15 2012 October 14 October 15 October 16 October 17 Mainly interested in: tv show network CL. Spagnolo Social conversational spaces 38 / 51
    • Proposed visualization | Example of interface (7) baumgartner bbc documentary fox news freefall MSNBC networks speed of light sound barrier tv show Felix Baumgartner Breaks Speed Of Sound read The Story Top participants Top disccussed themes for this conversation All Keywords People Organizations Places baumgartner fox news freefall speed of light sound barrier Bob74 311 Fans The headline is wrong. He didnt break the sound barrier. Hes :-) actually the first person to travel faster than the speed of light. I saw it on MSNBC. 00:16 PM on October 15 2012 Alice84 A 251 Fans NOT MSNBC, probably Fox News since they get more B :-( things wrong than any other network. 00:19 PM on October 15 2012 Chuck Conversation detail bbc documentary fox news tv show freefall 112 Fans • When a user :-| He speed of light is 186,000 mi/per sec. It was the speed of sound. A 00:21 PM on October 15 2012 Dean 342 Fans BBC is going to put a documentary next month about selected a specific :-| the jump, if anyone cares? fox news 00:32 PM on October 15 2012 conversation Eleanor66 12 Fans Knowing the BBC it will be biased and show • In a modal window :-( incorrect or incomplete facts. 00:34 PM on October 15 2012 Dean 342 Fans Oh, please. • More “traditional” :-| 22h00 23h00 00:35 PM on October 15 2012 00h00 01h00 02h00 Alice84 threaded view 251 Fans :-) Youre thinking of Fox News :-). 00:21 PM on October 15 2012 • For reading October 14 October 15 October 16 October 17 messages Mainly interested in: tv show network CL. Spagnolo Social conversational spaces 38 / 51
    • Proposed visualization | Example of interface (8) baumgartner bbc documentary fox news freefall MSNBC networks speed of light sound barrier tv show Felix Baumgartner Breaks Speed Of Sound read The Story Top participants Top disccussed themes for this conversation All Keywords People Organizations Places BBob74 baumgartner fox news freefall speed of light sound barrier 311 Fans Timeline The headline is wrong. He didnt break the sound barrier. Hes :-) actually the first person to travel faster than the speed of light. I saw it on MSNBC. 00:16 PM on October 15 2012 • For making sense of Alice84 251 Fans NOT MSNBC, probably Fox News since they get more B :-( bbc documentary fox news tv show a conversation with things wrong than any other network. 00:19 PM on October 15 2012 freefall Chuck its sub-threads 112 Fans :-| He speed of light is 186,000 mi/per sec. It was the speed of sound. • A vertical line A 00:21 PM on October 15 2012 Dean 342 Fans BBC is going to put a documentary next month about :-| fox news connets a the jump, if anyone cares? 00:32 PM on October 15 2012 Eleanor66 sub-conversation to 12 Fans Knowing the BBC it will be biased and show :-( incorrect or incomplete facts. the parent 00:34 PM on October 15 2012 Dean 342 Fans • The eye placeholder Oh, please. :-| 22h00 23h00 00:35 PM on October 15 2012 00h00 01h00 02h00 shows the Alice84 251 Fans :-) Youre thinking of Fox News :-). highlighted October 14 00:21 PM on October 15 2012 October 15 October 16 October 17 comment innetwork context Mainly interested in: tv show CL. Spagnolo Social conversational spaces 38 / 51
    • Proposed visualization | Example of interface (9) baumgartner bbc documentary fox news freefall MSNBC networks speed of light sound barrier tv show Felix Baumgartner Breaks Speed Of Sound read The Story Top participants Top disccussed themes for this conversation All Keywords People Organizations Places baumgartner fox news freefall speed of light sound barrier Bob74 311 Fans The headline is wrong. He didnt break the sound barrier. Hes :-) actually the first person to travel faster than the speed of light. I saw it on MSNBC. 00:16 PM on October 15 2012 Alice84 251 Fans NOT MSNBC, probably Fox News since they get more B :-( things wrong than any other network. bbc documentary fox news tv show 00:19 PM on October 15 2012 freefall Chuck 112 Fans :-| He speed of light is 186,000 mi/per sec. It was the speed of sound. A 00:21 PM on October 15 2012 Dean 342 Fans BBC is going to put a documentary next month about :-| the jump, if anyone cares? fox news 00:32 PM on October 15 2012 Eleanor66 12 Fans Knowing the BBC it will be biased and show :-( incorrect or incomplete facts. 00:34 PM on October 15 2012 Dean 342 Fans Oh, please. C :-| 22h00 23h00 00:35 PM on October 15 2012 00h00 01h00 02h00 Alice84 251 Fans Example of query :-) Youre thinking of Fox News :-). with user selected 00:21 PM on October 15 2012 October 14 October 15 October 16 October 17 Mainly interested in: tv show network C themesL. Spagnolo Social conversational spaces 38 / 51
    • Proposed visualization | Conversations (1) r Conversation line for a C = m0 ← m1 , m2 , . . . , ml − Kyoto ecology green economy • Length: duration of the conversion λ(C) = uλ · (ts(ml ) − ts(m0 )) uλ : visualization scale ts(m0 ), ts(ml ): timestamps of first and last message • Thickness: overall interestingness score θ(C, q) = θmin + (θmax − θmin ) ξξ(C,q)−ξmin (q) max (q)−ξmin (q) ξmin (q), ξmax (q): min and max interestingness score θmin , θmin :min and max thickness • Colour: overall sentiment score (interpolated)L. Spagnolo Social conversational spaces 39 / 51
    • Proposed visualization | Conversations (2) Coloured circles/dots: messages or sub-threads • Size: Interestingness score • Colour: sentiment score Coloured tags: (most) relevant themes for the conversation • Font size: degree of relevance of the conversation • Colour: sentiment score User avatars: (most) active participants in the conversation • Image size: degree of involvement or popularityL. Spagnolo Social conversational spaces 40 / 51
    • Proposed visualization | Theme cloudsSummarizing themes andopinions • Font size: Overall relevance of themes • Colour: sentiment score (interpolated) Red → negative Yellow → negative Green → positive • Overline colour: consensus Dark → divergence Opinions and themes of discussions on a White → consensus HuffingtonPost article about Obama’s Health Care reformL. Spagnolo Social conversational spaces 41 / 51
    • Proposed visualization | ParticipantsSummarizing user popularity • Font and image size: Overall user popularity score (authoritativeness + influence) • in specific case, metrices for broadcasted messages are used → eq. (3) Weighted list of most popular users on Huffington Post (estimated from a small set of analysed discussions).L. Spagnolo Social conversational spaces 42 / 51
    • Outline Conversational spaces Background The user experience: scenarios of usage Proposed model Proposed visualization Conclusions and future workL. Spagnolo Social conversational spaces 43 / 51
    • Conclusions and future work (1) Improving the user experience of contents in social spaces • affects the way users perceive others’ contributions • but it may also change the way they interact by adding their own comments What actually happens if some discussions (possibly interesting for the user) are highlighted? Users are attracted towards highlighted conversations → conversations gain extra participation → their interestingness additionally increases On the contrary, discussions with a lower rank → likely to receive few new comments → few chances of becoming more popular Desired effect in some cases In other coext (education?) contexts, more thoughtful discussions with fewer and less frequent comments may be incorrectly penalized • Investigation on real case studies is neededL. Spagnolo Social conversational spaces 44 / 51
    • Conclusions and future work (2) Changes in interest towards conversations possibly affects past discussions too • What happens if users are llowed to browse part conversations and harvest knowledge from them? • Are “cold” conversations more likely to be “brought again to life”? Other future research aspect: improving automatic classification of messages and conversations • Real life applications may provide feedback for enhancement and fine-tuning of mining tools • Reinforcement learning strategy: letting the users check and modify the automatic classification each time they post a new message e.g. by removing false positives concerning themes ...or adjusting the sentiment scoreL. Spagnolo Social conversational spaces 45 / 51
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