Designing social conversational spaces:enhancing communicative and emotional impact

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, 2012

L. Spagnolo Social conversational spaces 1 / 51

Outline

Conversational spaces

Background

The user experience: scenarios of usage

Proposed model

Proposed visualization

Conclusions and future work



With 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
items


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.


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 post


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 retrieval


Outline


Background


Proposed model




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 past


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)

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
topic


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)


Outline


Background


Proposed model




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.


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.


Outline


Background


Proposed model




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).



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.



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
−


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 users



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.


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.


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) = ∅


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”)



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 )



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)
}



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)

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 interestingness


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.


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.



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 density



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.


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 variance


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∈M



Sentiment score for theme t, w.r.t. a set of messages M:

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:

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))


Outline


Background


Proposed model




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.

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

:-(
didn't break the sound
barrier. He's 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




Of Sound read




:-(

A D Bob74
311 Fans
MSNBC.


The

information
item
(Huﬃngton Post
article) around
22h00 23h00 00h00 01h00 02h00
which
discussionsOctober 14 October 15 October 16 October 17

revolve



Of Sound read



B The headline is wrong. He

:-(

The most D Bob74
311 Fans
MSNBC.

active

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




Of Sound read




:-(

D Bob74
311 Fans
MSNBC.
C

Coloured tag barrier

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




Of Sound read

The Story A Top participants B Top disccussed themes C DAll Keywords People Organizations Places

Timeline
• Discussion
ﬂows as

:-(

D Bob74
311 Fans
MSNBC.
arrows

baumgartner big deal red bull sound barrier

baumgartner fox news freefall speed of light sound barrier
• For each
conversation:
main threads
(circles),
participants,
themes
22h00 23h00 00h00 01h00 02h00
• Callouts:
providing
preview



baumgartner bbc documentary fox news freefall MSNBC networks speed of light
sound barrier tv show

Of Sound read

The Story Top participants Top disccussed themes for this conversation All Keywords People Organizations Places

Bob74
311 Fans
The headline is wrong. He didn't break the sound barrier. He's

:-)
actually the first person to travel faster than the speed of light. I
saw it on MSNBC.

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

Dean
342 Fans
BBC is going to put a documentary next month about
:-|

the jump, if anyone cares? fox news

Eleanor66
12 Fans
Knowing the BBC it will be biased and show
:-(

incorrect or incomplete facts.

Dean
342 Fans
Oh, please.
:-|

22h00 23h00
00h00 01h00 02h00
Alice84
251 Fans
:-)

You're thinking of Fox News :-).


Mainly interested in: tv show network
C




Of Sound read


Bob74
311 Fans

:-)
saw it on MSNBC.

Alice84 A
251 Fans
:-(

things wrong than any other network.
Chuck
Conversation detail
bbc documentary fox news tv show
freefall

112 Fans

• When a user
:-|

speed of sound.

A

Dean
342 Fans
selected a speciﬁc
:-|

conversation
Eleanor66
12 Fans
• In a modal window
:-(


Dean
342 Fans
Oh, please.
• More “traditional”
:-|

22h00 23h00
00h00 01h00 02h00
Alice84
threaded view
251 Fans
:-)

• For reading
messages
C




Of Sound read


BBob74
311 Fans

Timeline

:-)
saw it on MSNBC.

• For making sense of
Alice84
251 Fans
:-(

bbc documentary fox news tv show
a conversation with
things wrong than any other network.
Chuck

its sub-threads
112 Fans
:-|

speed of sound.

• A vertical line A

Dean
342 Fans
:-|

fox news
connets a
the jump, if anyone cares?

Eleanor66
sub-conversation to
12 Fans
:-(


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
:-)


highlighted
October 14

October 15 October 16 October 17
comment innetwork context
Mainly interested in: tv show
C




Of Sound read


Bob74
311 Fans

:-)
saw it on MSNBC.

Alice84
251 Fans
:-(

things wrong than any other network. bbc documentary fox news tv show
Chuck
112 Fans
:-|

speed of sound.

A

Dean
342 Fans
:-|


Eleanor66
12 Fans
:-(


Dean
342 Fans
Oh, please.
C
:-|

22h00 23h00
00h00 01h00 02h00
Alice84
251 Fans Example of query
:-)


with user selected


C themes

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)


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
User avatars: (most) active participants in the conversation
• Image size: degree of involvement or popularity


Proposed visualization | Theme clouds

Summarizing themes and
opinions
• Font size: Overall
relevance of themes
(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 reform


Proposed visualization | Participants

Summarizing 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).


Outline


Background


Proposed model




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 needed

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 score

References I

André, P., Schraefel, M., Dix, A., and White, R. (2011). Expressing well-being
online: towards self-reflection and social awareness. In Proceedings of the
2011 iConference, pages 114–121. ACM.
Bakshy, E., Hofman, J., Mason, W., and Watts, D. (2011). Everyone’s an
influencer: quantifying influence on twitter. In Proceedings of the fourth
ACM international conference on Web search and data mining, pages 65–74.
ACM.
Barbagallo, D., Bruni, L., Francalanci, C., Giacomazzi, P., Fuchs, M., Ricci, F.,
Cantoni, L., et al. (2012). An empirical study on the relationship between
twitter sentiment and influence in the tourism domain. In Information and
communication technologies in tourism 2012, Helsingborg, Sweden, January
25-27, 2012. Springer-Verlag Wien.
Bruni, L., Francalanci, C., Giacomazzi, P., and Petrovich, F. (2012). The role
of multimedia content in determining the virality of social media information:
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