10 slides emotions

Chair of Systems Design www.sg.ethz.ch

Business Applications of Emotions on the Internet

David GarciaInternet of Things seminar Zurich

May 26, 2011

David Garcia Chair of Systems Design www.sg.ethz.chThe Internet of Emotions

Outline

1 Impact of Internet emotions

2 Online Emotions Research

3 Emotions in the Internet of Things

4 Viral Marketing

Internet of Things seminar Zurich May 26, 2011 2 / 24

David Garcia Chair of Systems Design www.sg.ethz.chThe Internet of Emotions Impact of Internet emotions

Impact of Internet emotions




4 Viral Marketing



Emotions in the IoT seminar

Behavioral paradoxes

Emotions as a possible answer

1 evolutionary mechanism for activity (why do we party?)

2 suboptimal / irrational behavior (why dont we save energy?)

1 Activity trigger examples

Travel insurance purchased by fearIncentive mechanism for user-driven mashups

2 Emotional feedback

Persuasive toothbrush, colorful energy consumption displays, musicalrabbits, shoes connected to your iPod...Risk factor for technology acceptance: importance of user interface



What are we talking about?

Collective emotions

Emotional state shared by a large number of members of an onlinecommunity at a given time, created by the exchange of emotionalinformation and the influence of external factors.



Definition of emotions

reex reactions neural responses psycological states cognitive processesmoodcore aectphysiological level

lifetime behaviorpersonality traits

log t...

Definition: Psychological states of high relevance for the individualthat imply cognitive and physiological effects

Different levels of description:Physiological level: brain parts active during emotion (e.g amigdala)Core affect: basic psychological components of emotionsMood: long-term psychological states related to cognitive antecedentsPersonality traits: lifelong factors of personality sustained by emotions



Internet communication

inte

ract

ion

close friendsloose contacts

Web 2.0

potential usersThe Internet as a tool to overcome cognitive biases:

Web 2.0 allows to reach more users instantaneously

large number of loose contacts: de facto anonymity

persistence of communication



Two types of Internet Collective Emotions

Exogeneous vs Endogeneous collective emotions

Michael Jacksons death

triggered by an external event

shared sadness

the Boxxy phenomenon

created in online communities

strong emergent polarization



Benefits and risks of Cyberemotions

Benefits

1 CE foster emergence of online collaboration (product reviews)

2 CE support collective decisions (facebook revolutions)

3 CE can mitigate civil conflicts

Risks

1 CE create unforseeable activity bursts

2 CE negative impact on reputation of users/website

3 CE can amplify political conflicts (Kirgizstan riots 2010)

4 CE influence economic markets (stock exchange)if social/economic explanation is missing, emotions are hypothesizedamplifies hysteria crashes, wishful thinking bubbles


David Garcia Chair of Systems Design www.sg.ethz.chThe Internet of Emotions Online Emotions Research

Online Emotions Research




4 Viral Marketing



Modeling of collective emotions

Microdynamics lead to macroscopic behavior

Agent-based models based in psychology show the emergence ofcollective emotions.

(An agent-based model of collective emotions in online communities. F. Schweitzer

& D. Garcia)



EU Project on Cyber Emotions (CE) http://www.cyberemotions.eu/

To understand the role of collective emotions in creating, forming andbreaking-up ICT mediated communities as a spontaneous emergentbehaviour occurring in complex techno-social networks

Funded by 7th Framework Programme (start 02/2009)

Collaboration of 8 European universities for 4 years


David Garcia Chair of Systems Design www.sg.ethz.chThe Internet of Emotions Emotions in the Internet of Things

Emotions in the Internet of Things




4 Viral Marketing



The Gnomes Business plan

Internet

Things?

Profit!

Models play a key role in Internet of Things applications




InternetThings

?Profit!





InternetThings

?

Profit!





InternetThings

?Profit!




The Internet of Emotions Framework

F

F

SS

S

S

s

s

s MF

FF

F

1 Things: humans in a socialenvironment

2 Sensors

individualrelationscollective

3 Models of human behavior andcommunication

4 Feedback mechanisms toinfluence and adapt to thecommunity



Emotion Sensor Technologies

1 Software

text classifiersbehavioral patterns(intermessage delays, responsesignatures...)

2 Physiological sensors

skin conductance andtemperature, heart rate...facial recognition: nviso

IBM BluEyes project

How can we make computerssee and feel?



Models for the Internet of Emotions

Agent-based models

Network cascades models

Behavioral models

C

Pareto

A

B

f2(A) < f2(B)

f1

f2

f1(A) > f1(B)

Multidimensional optimization

Output: monitorization, prediction, normative rules



Feedback mechanisms

ICT platform redesign

Content creation to driveactivity momentum

Emotional interaction with user:

direct: dialog systems, automaticinformationindirect: music, colors...

Incentive mechanisms forcollaboration


David Garcia Chair of Systems Design www.sg.ethz.chThe Internet of Emotions Viral Marketing

Internet marketing




4 Viral Marketing



Internet marketing size

$17 billion spent on online marketing in the US in 2006(pricewaterhousecoopers), before social networks came into playIn 2008, Barack Obama raised $1 million in one day of onlinemarketing28% of the population has access to the Internet (2 billion, 2011)E-commerce is estimated to be 188 billion in 2011 (eMarketers)Facebook, twitter, 4square are still to monetize.



Viral marketing

Approaches to the product acceptance problem from the side of(online) social network interactions

Virality goes beyond content, being funny is not enough

Previous models of viral marketing in social networks were static

consumer states were just buying/not buying

Network influences are more complex than just dominant set



A model for emotional viral marketing

expr. alongdirected links

cascadesmodel

oers forinuential

users

sensor data: user expression alongsocial links (messages, wall posts...)

model: network cascades, parameterestimation from:

1 social network topology2 tendency patterns3 link strength inference

feedback: decision support for cascadecreation:

set of users to motivateglobal properties of the system: naturalfrequency, self-organized criticality...



Emotional viral marketing process

Small world social network

parameters: avg degree, avg path length

Three states:

1 S: Susceptible - user is listening toothers expression

2 I: Infected - user is in an emotional statean expresses it

3 R: Recovered - user has relaxed and isnot excited for a period of time

2

86

67227

258

59

49

281

37

193

118

240

168

81

279

62

230

13

41

11

98

162

226

194

103

225

107

69

31

288

252

115

177

202

209

267

47

294

126

77

25

91

171

132

10

190

250

290231

92

100

148

119

164

253

158

28

124

205

151

45

128

82

125

187

113

133138

93

17

14

286

234

60

255

285

130

116

105

280

163

104

70

42

50

140

76

185

173

122

19

232

157

34

35

3026

166

292

273

154

239

246

170

20

139

24

68

201

3

160

29206

199

247

87

73

58

195

127

263

216

4

251

197

276

178

287

83

238

169

144

135

84

152

153

265

275

36

297

16

78

244

97

15

71

293

149

241

57

269

208

117

289

137

214

85

184

52

203

257

196

121

212

200

5

129

12

224

64

262

298

268

182

143

51

296

295

95

79

23174

89

183

24246

210

99

66

21

7

245

27

219

56

75

277

120

191

283

220

80108

217

192

181

136

18

114

106

291

141

74

111

284

211

207

256

55

112

110

159

54

134

272

150

146

88

188

43

101

48

9

32

180189

155

249

1

61

22

237

109

44

260

266

235

213

63

123

228

215

90

165

27839

243

102

186

175

299

264

254

221

6

96

147

38

274

198

204

0

222248

172

72

270

131

65

167

161

229

261

33

40

145

53

179

282

271

233

156

259

218

223

142

236

176

94

8

I R: after time iR S: after time rS I: infection proportional to amount of infected neighbors



Discusion time!

1 What other behavioral paradoxes do you see relevant for emotionsin the IoT?

2 Other applications of the Internet of Emotions framework?

online community managementworkgroup emotion optimizationreal-time monitoring of crowd emotions

3 What technological distances can be narrowed by emotion sensors?

4 Applications of these principles outside the cyberspace

5 Some entrepreneurship?


Impact of Internet emotionsOnline Emotions ResearchEmotions in the Internet of ThingsViral Marketing

10 slides emotions

Documents

potential usersthe internet

applications of emotions

things seminar zurich

cognitive biases

longterm psychological

influence of external

large number of members

given time