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ONLINE MULTITASKING
AND USER ENGAGEMENT
CIKM 2013
In	
  collabora*on	
  with:	
  
Mounia	
  Lalmas,	
  	
  
Ricardo	
  Baeza-­‐Yates,	
  	
  
George	
  Dupret	
  
Jane%e	
  Lehmann	
  
outline
1.  Mo%va%on	
  
How	
  do	
  users	
  browse	
  the	
  web	
  today?	
  
	
  
	
  
2.  Characteris%cs	
  of	
  online	
  mul%tasking	
  
Ac2vity	
  during	
  and	
  between	
  visits	
  
	
  
	
  
3.  Measuring	
  online	
  mul%tasking	
  
Defini2on	
  of	
  new	
  metrics,	
  case	
  study	
  
	
  
Lights	
  on	
  by	
  JC*+A!	
  
How	
  do	
  users	
  
browse	
  the	
  Web	
  
today?	
  
leC	
  by	
  	
  [	
  embr	
  ]	
  	
  
ONLINE MULTITASKING
4	
  JaneGe	
  Lehmann	
   Mo2va2on	
  
Browsing	
  the	
  “old	
  way”	
  
facebook	
   news	
   news	
  news	
   news	
   mail	
  
1min	
   2min	
   1min	
   3min	
  
Dwell	
  2me	
  during	
  a	
  visit	
  on	
  a	
  news	
  site:	
  
7min	
  on	
  average	
  
news	
  site	
  
ONLINE MULTITASKING
5	
  JaneGe	
  Lehmann	
   Mo2va2on	
  
Nowadays	
  
news	
   facebook	
   mail	
  news	
   news	
   news	
  
1min	
   2min	
   1min	
   3min	
  
Dwell	
  2me	
  during	
  a	
  visit	
  on	
  a	
  news	
  site:	
  
2.33min	
  on	
  average	
  (1min	
  |	
  3min	
  |	
  3min)	
  
ONLINE MULTITASKING
6	
  JaneGe	
  Lehmann	
   Mo2va2on	
  
•  Users	
  switch	
  between	
  sites,	
  to	
  do	
  related	
  or	
  totally	
  unrelated	
  tasks	
  
	
  
	
  
	
  
•  E.	
  Herder	
  [1]:	
  
»  75%	
  of	
  sites	
  are	
  visited	
  more	
  than	
  once	
  
»  74%	
  of	
  revisits	
  are	
  performed	
  within	
  a	
  session	
  
	
  
Measuring	
  browsing	
  behavior	
  can	
  lead	
  to	
  incorrect	
  conclusions.	
  
	
  
[1]	
  E.	
  Herder.	
  Characteriza*ons	
  of	
  user	
  web	
  revisit	
  behavior.	
  In	
  LWA,	
  2005.	
  
Characteris%cs	
  
of	
  online	
  
mul%tasking	
  
Danboard's	
  Messy	
  Home	
  by	
  Mullenkedheim	
  
DATA SET
Interac%on	
  data	
  
•  July	
  2012	
  
•  2.5M	
  users	
  
•  785M	
  page	
  views	
  
	
  
•  We	
  defined	
  a	
  new	
  naviga2on	
  model	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
(see	
  paper	
  for	
  detail)	
  
	
  
	
  
•  Categoriza2on	
  of	
  the	
  most	
  frequent	
  accessed	
  sites	
  
(e.g.	
  mail,	
  news,	
  shopping)	
  
»  11	
  categories	
  (news),	
  33	
  subcategories	
  (e.g.	
  news	
  
finance,	
  news	
  society)	
  
»  760	
  sites	
  from	
  70	
  countries/regions	
  
	
  
	
   8	
  JaneGe	
  Lehmann	
   Characteris2cs	
  
Visit activity
Visit	
  frequency 	
  	
  
9	
  JaneGe	
  Lehmann	
   Characteris2cs	
  
Mul%tasking	
  depends	
  on	
  the	
  site	
  under	
  
considera%on	
  
	
  
•  Social	
  media	
  sites	
  are	
  revisited	
  the	
  
most	
  
•  News	
  (tech)	
  sites	
  are	
  the	
  least	
  	
  
revisited	
  sites	
  
news (finance)
news (tech)
social media
mail
2.09
1.76
2.28
2.09
4.65
1.59
4.78
4.61
#Visits
(avg sd)
Visit activity
Ac%vity	
  between	
  visits	
  
	
  	
  
10	
  JaneGe	
  Lehmann	
   Characteris2cs	
  
Differences	
  in	
  the	
  absence	
  %me	
  
	
  
•  50%	
  of	
  sites	
  are	
  revisited	
  aCer	
  less	
  
than	
  1min	
  
	
  	
  	
  	
  	
  -­‐	
  Interrup*on	
  of	
  a	
  task	
  
•  There	
  are	
  revisits	
  aCer	
  a	
  long	
  break	
  	
  	
  	
  	
  	
  	
  
-­‐	
  Returning	
  to	
  a	
  site	
  to	
  perform	
  a	
  new	
  
task	
  
0.00
0.25
0.50
0.75
1.00
10 2
10 1
100
101
102
mail
social media
news (finance)
news (tech)
Cumulativeprobability
Absence time [min]
*	
   v2	
  v1	
   *	
   v3	
  
*	
  -­‐	
  absence	
  2me	
  
Visit activity
Ac%vity	
  paLern	
  
	
  	
  
11	
  JaneGe	
  Lehmann	
   Characteris2cs	
  
•  Four	
  types	
  of	
  "aGen2on	
  shiCs”	
  
•  Complex	
  cases	
  refer	
  to	
  no	
  
specific	
  paGern	
  or	
  repeated	
  
paGern	
  
•  Successive	
  visits	
  can	
  belong	
  
together	
  (i.e.,	
  to	
  the	
  same	
  task)	
  
0.23
0.28
0.33
mail sites
news (finance) sites news (tech) sites
social media sites
decreasing attention increasing attention
constant attention complex attention
Proportionoftotal
dwelltimeonsite
p-value = 0.09
m = -0.01
p-value = 0.07
m = -0.02
p-value = 0.79
m = 0.00
0.23
0.28
0.33
Proportionoftotal
dwelltimeonsite
Danboard	
  by	
  sⓘndy°	
  
Measuring	
  	
  
online	
  
mul%tasking	
  	
  
Cumulative activity
Cumula%ve	
  ac%vity	
  
	
  
	
  
	
  
	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  vi 	
  Browsing	
  ac2vity	
  during	
  the	
  ith	
  visit	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  ivi 	
  Browsing	
  ac2vity	
  between	
  the	
  (i-­‐1)th	
  and	
  ith	
  visit	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  k=3 	
  Rescaling	
  factor	
  for	
  ivi	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  m 	
  Browsing	
  ac2vity	
  (e.g.	
  dwell	
  2me,	
  page	
  views)	
  
	
  
Assump%on:	
  
If	
  users	
  return	
  aCer	
  short	
  2me,	
  they	
  return	
  to	
  con2nue	
  with	
  same	
  task.	
  
If	
  users	
  return	
  aCer	
  longer	
  2me,	
  they	
  return	
  to	
  perform	
  a	
  new	
  task	
  -­‐	
  an	
  indica2on	
  of	
  loyalty	
  to	
  
the	
  site.	
  
	
  
13	
  JaneGe	
  Lehmann	
   Metrics	
  
CumActm,k = log10 (v1 + ivi
k
•vi
i=2
n
∑ )
iv2	
   v2	
  v1	
   iv3	
   v3	
  
Cumulative activity
Cumula%ve	
  ac%vity	
  
	
  
	
  
	
  
	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  vi 	
  Browsing	
  ac2vity	
  during	
  the	
  ith	
  visit	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  ivi 	
  Browsing	
  ac2vity	
  between	
  the	
  (i-­‐1)th	
  and	
  ith	
  visit	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  k=3 	
  Rescaling	
  factor	
  for	
  ivi	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  m 	
  Browsing	
  ac2vity	
  (e.g.	
  dwell	
  2me,	
  page	
  views)	
  
	
  
Assump%on:	
  
If	
  users	
  return	
  aCer	
  short	
  2me,	
  they	
  return	
  to	
  con2nue	
  with	
  same	
  task.	
  
If	
  users	
  return	
  aCer	
  longer	
  2me,	
  they	
  return	
  to	
  perform	
  a	
  new	
  task	
  -­‐	
  an	
  indica2on	
  of	
  loyalty	
  to	
  
the	
  site.	
  
	
  
14	
  JaneGe	
  Lehmann	
   Metrics	
  
CumActm,k = log10 (v1 + ivi
k
•vi
i=2
n
∑ )
iv2	
   v2	
  v1	
   iv3	
   v3	
  
Cumulative activity
Cumula%ve	
  ac%vity	
  
	
  
	
  
	
  
	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  vi 	
  Browsing	
  ac2vity	
  during	
  the	
  ith	
  visit	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  ivi 	
  Browsing	
  ac2vity	
  between	
  the	
  (i-­‐1)th	
  and	
  ith	
  visit	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  k=3 	
  Rescaling	
  factor	
  for	
  ivi	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  m 	
  Browsing	
  ac2vity	
  (e.g.	
  dwell	
  2me,	
  page	
  views)	
  
	
  
Assump%on:	
  
If	
  users	
  return	
  aCer	
  short	
  2me,	
  they	
  return	
  to	
  con2nue	
  with	
  same	
  task.	
  
If	
  users	
  return	
  aCer	
  longer	
  2me,	
  they	
  return	
  to	
  perform	
  a	
  new	
  task	
  -­‐	
  an	
  indica2on	
  of	
  loyalty	
  to	
  
the	
  site.	
  
	
  
15	
  JaneGe	
  Lehmann	
   Metrics	
  
CumActm,k = log10 (v1 + ivi
k
•vi
i=2
n
∑ )
iv2	
   v2	
  v1	
   iv3	
   v3	
  
v1	
  +	
  v2	
  +	
  v3	
  
	
  
	
  
Cumulative activity
Cumula%ve	
  ac%vity	
  
	
  
	
  
	
  
	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  vi 	
  Browsing	
  ac2vity	
  during	
  the	
  ith	
  visit	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  ivi 	
  Browsing	
  ac2vity	
  between	
  the	
  (i-­‐1)th	
  and	
  ith	
  visit	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  k=3 	
  Rescaling	
  factor	
  for	
  ivi	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  m 	
  Browsing	
  ac2vity	
  (e.g.	
  dwell	
  2me,	
  page	
  views)	
  
	
  
Assump%on:	
  
If	
  users	
  return	
  aCer	
  short	
  2me,	
  they	
  return	
  to	
  con2nue	
  with	
  same	
  task.	
  
If	
  users	
  return	
  aCer	
  longer	
  2me,	
  they	
  return	
  to	
  perform	
  a	
  new	
  task	
  -­‐	
  an	
  indica2on	
  of	
  loyalty	
  to	
  
the	
  site.	
  
	
  
16	
  JaneGe	
  Lehmann	
   Metrics	
  
CumActm,k = log10 (v1 + ivi
k
•vi
i=2
n
∑ )
iv2	
   v2	
  v1	
   iv3	
   v3	
  
Cumulative activity
Cumula%ve	
  ac%vity	
  
	
  
	
  
	
  
	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  vi 	
  Browsing	
  ac2vity	
  during	
  the	
  ith	
  visit	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  ivi 	
  Browsing	
  ac2vity	
  between	
  the	
  (i-­‐1)th	
  and	
  ith	
  visit	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  k=3 	
  Rescaling	
  factor	
  for	
  ivi	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  m 	
  Browsing	
  ac2vity	
  (e.g.	
  dwell	
  2me,	
  page	
  views)	
  
	
  
Assump%on:	
  
If	
  users	
  return	
  aCer	
  short	
  2me,	
  they	
  return	
  to	
  con2nue	
  with	
  same	
  task.	
  
If	
  users	
  return	
  aCer	
  longer	
  2me,	
  they	
  return	
  to	
  perform	
  a	
  new	
  task	
  -­‐	
  an	
  indica2on	
  of	
  loyalty	
  to	
  
the	
  site.	
  
	
  
17	
  JaneGe	
  Lehmann	
   Metrics	
  
CumActm,k = log10 (v1 + ivi
k
•vi
i=2
n
∑ )
iv2	
   v2	
  v1	
   iv3	
   v3	
  
v1	
  +	
  (iv2)3Ÿ	
  v2	
  +	
  (iv3)3Ÿ	
  v3	
  
	
  
	
  
Activity pattern
ALen%on	
  shiN	
  and	
  range	
  
	
  
	
  
	
  
	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  n=4	
   	
  Number	
  of	
  visits	
  in	
  session	
  	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  σ 	
  Variance	
  in	
  the	
  visit	
  ac2vity	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  μ 	
  Average	
  of	
  the	
  visit	
  ac2vity	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
  inv 	
  Modifica2on	
  of	
  the	
  “Inversion	
  number”	
  	
  
	
  
	
  
Descrip%on:	
  
AGShiC	
  models	
  the	
  shiC	
  of	
  aGen2on	
  in	
  the	
  browsing	
  ac2vity	
  
AGRange	
  describes	
  fluctua2ons	
  in	
  the	
  browsing	
  ac2vity	
  
	
  
18	
  JaneGe	
  Lehmann	
   Metrics	
  
AttShiftm,n =
invm,n − minInvm,n
| maxInvm,n |− | minInvm,n |
AttRangem,n =
σ (Vm,n )
µ(Vm,n )
Activity pattern
ALen%on	
  shiN	
  and	
  range	
  
19	
  JaneGe	
  Lehmann	
   Metrics	
  
-­‐1	
   0	
   1	
  
0	
  
constant	
   constant	
   constant	
  
>	
  0	
  
decreasing	
   complex	
   increasing	
  
AUen*on	
  shiV	
  
AUen*on	
  range	
  
Comparing	
  the	
  ranking	
  of	
  the	
  sites	
  
•  Visitdt	
  –	
  Dwell	
  2me	
  during	
  a	
  visit	
  
•  Sessiondt	
  –	
  Dwell	
  2me	
  during	
  a	
  session	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
Ø  Visitdt	
  and	
  Sessiondt	
  correlate	
  
Ø  Otherwise	
  no	
  correla2on	
  à	
  the	
  other	
  metrics	
  capture	
  different	
  aspects	
  of	
  
browsing	
  behavior	
  
Comparing metrics
20	
  JaneGe	
  Lehmann	
   Metrics	
  
Visitdt	
   Sessiondt	
   CumActdt	
   ALShiNdt	
  
Sessiondt	
   0.57	
  
CumActdt	
   -­‐0.04	
   0.24	
  
ALShiNdt	
   0.09	
   0.22	
   0.02	
  
ALRangedt	
   -­‐0.01	
   -­‐0.01	
   -­‐0.26	
   0.19	
  
“Models”	
  of	
  browsing	
  behavior	
  
•  Clustering	
  of	
  sites	
  using	
  mul2tasking	
  and	
  standard	
  engagement	
  metrics:	
  
•  CumActdt,	
  AGShiCdt,	
  AGRangedt	
  
•  Visitdt,	
  Sessiondt	
  
	
  
•  We	
  iden2fied	
  five	
  cluster:	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
Models of browsing behavior
21	
  JaneGe	
  Lehmann	
   Metrics	
  
C4: 74 sites
0.25
-0.25
0.75
-0.75
C5: 166 sites
0.25
-0.25
0.75
-0.75
C3: 156 sites
0.25
-0.25
0.75
-0.75
C2: 108 sites
0.25
-0.25
0.75
-0.75
C1: 172 sites
0.25
-0.25
0.75
-0.75
Visitdt
[min] CumActdt,3
AttShiftdt,4
AttRangedt,4
Sessiondt
[min]
Models of browsing behavior
22	
  JaneGe	
  Lehmann	
   Metrics	
  
Visitdt
[min] CumActdt,3
AttShiftdt,4
AttRangedt,4
Sessiondt
[min]
C2: 108 sites
auctions, front page,
shopping, dating
0.25
-0.25
0.75
-0.75
C1: 172 sites
mail, maps, news,
news (soc.)
0.25
-0.25
0.75
-0.75
One	
  task	
  during	
  a	
  session	
  
	
  
§  High	
  dwell	
  2me	
  per	
  visit	
  and	
  during	
  
the	
  whole	
  session	
  
	
  
§  Users	
  return	
  to	
  con2nue	
  a	
  task	
  (short	
  
absence	
  2me)	
  
	
  
§  C1:	
  aGen2on	
  is	
  shiCing	
  to	
  another	
  site	
  
§  C2:	
  aGen2on	
  is	
  shiCing	
  slowly	
  towards	
  
the	
  site	
  
C4: 74 sites
front page, search,
download
C3: 156 sites
auctions, search,
front page, shopping
0.25
-0.25
0.75
-0.75
0.25
-0.25
0.75
-0.75
Models of browsing behavior
23	
  JaneGe	
  Lehmann	
   Metrics	
  
Several	
  tasks	
  during	
  a	
  session	
  
	
  
§  Users	
  perform	
  several	
  tasks	
  on	
  these	
  
sites	
  during	
  a	
  session	
  
§  No	
  simple	
  ac2vity	
  paGern	
  	
  
§  C3:	
  Dwell	
  2me	
  per	
  visit	
  is	
  low,	
  but	
  the	
  
dwell	
  2me	
  per	
  session	
  is	
  high	
  
	
  
Visitdt
[min] CumActdt,3
AttShiftdt,4
AttRangedt,4
Sessiondt
[min]
C5: 166 sites
service, download,
blogging, news (soc.)
0.25
-0.25
0.75
-0.75
Models of browsing behavior
24	
  JaneGe	
  Lehmann	
   Metrics	
  
Sites	
  with	
  low	
  ac%vity	
  
	
  
§  Users	
  do	
  not	
  spend	
  a	
  lot	
  of	
  2me	
  on	
  
these	
  sites	
  
	
  
§  Time	
  between	
  visits	
  is	
  short	
  
	
  
§  AGen2on	
  is	
  shiCing	
  towards	
  the	
  site	
  
Visitdt
[min] CumActdt,3
AttShiftdt,4
AttRangedt,4
Sessiondt
[min]
C2: 108 sites
auctions, front page,
shopping, dating
0.25
-0.25
0.75
-0.75
C3: 156 sites
auctions, search,
front page, shopping
0.25
-0.25
0.75
-0.75
Models of browsing behavior
25	
  JaneGe	
  Lehmann	
   Metrics	
  
Browsing	
  behavior	
  can	
  differ	
  between	
  
sites	
  of	
  the	
  same	
  category	
  
	
  
§  C2:	
  users	
  visit	
  site	
  once	
  to	
  perform	
  
their	
  task	
  
§  C3:	
  users	
  visit	
  site	
  several	
  2mes	
  to	
  
perform	
  task(s)	
  
Visitdt
[min] CumActdt,3
AttShiftdt,4
AttRangedt,4
Sessiondt
[min]
SUMMARY and Future Work
JaneGe	
  Lehmann	
   26	
  
•  Online	
  mul2tasking	
  affects	
  the	
  way	
  users	
  access	
  sites	
  –	
  Standard	
  metrics	
  
do	
  not	
  capture	
  this!!!	
  
•  We	
  defined	
  metrics	
  that	
  describe	
  different	
  aspects	
  of	
  mul2tasking	
  
•  CumAct	
  accounts	
  for	
  the	
  2me	
  between	
  visits	
  
•  AGShiC,	
  AGRange	
  describe	
  aGen2on	
  shiCs	
  
•  We	
  showed	
  that	
  mul2tasking	
  depends	
  on	
  the	
  site	
  under	
  considera2on	
  
	
  
Future	
  work:	
  
•  Can	
  we	
  improve	
  the	
  defini2on	
  of	
  a	
  task?	
  
•  How	
  does	
  mul2tasking	
  affect	
  other	
  metrics,	
  such	
  as	
  bounce	
  rate	
  and	
  click-­‐
through	
  rate?	
  
•  Does	
  mul2tasking	
  differ	
  in	
  different	
  countries?	
  
Summary	
  
Janette Lehmann
Universitat Pompeu Fabra, Spain
lehmannj@acm.org
Mounia Lalmas
Yahoo Labs London
mounia@acm.org
George Dupret
Yahoo Labs Sunnyvale
gdupret@yahoo-inc.com
Ricardo Baeza-Yates
Yahoo Labs Barcelona
rbaeza@acm.org
Online 
Multitasking
+
User
Engagement

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Online Multitasking and User Engagement

  • 1. ONLINE MULTITASKING AND USER ENGAGEMENT CIKM 2013 In  collabora*on  with:   Mounia  Lalmas,     Ricardo  Baeza-­‐Yates,     George  Dupret   Jane%e  Lehmann  
  • 2. outline 1.  Mo%va%on   How  do  users  browse  the  web  today?       2.  Characteris%cs  of  online  mul%tasking   Ac2vity  during  and  between  visits       3.  Measuring  online  mul%tasking   Defini2on  of  new  metrics,  case  study     Lights  on  by  JC*+A!  
  • 3. How  do  users   browse  the  Web   today?   leC  by    [  embr  ]    
  • 4. ONLINE MULTITASKING 4  JaneGe  Lehmann   Mo2va2on   Browsing  the  “old  way”   facebook   news   news  news   news   mail   1min   2min   1min   3min   Dwell  2me  during  a  visit  on  a  news  site:   7min  on  average   news  site  
  • 5. ONLINE MULTITASKING 5  JaneGe  Lehmann   Mo2va2on   Nowadays   news   facebook   mail  news   news   news   1min   2min   1min   3min   Dwell  2me  during  a  visit  on  a  news  site:   2.33min  on  average  (1min  |  3min  |  3min)  
  • 6. ONLINE MULTITASKING 6  JaneGe  Lehmann   Mo2va2on   •  Users  switch  between  sites,  to  do  related  or  totally  unrelated  tasks         •  E.  Herder  [1]:   »  75%  of  sites  are  visited  more  than  once   »  74%  of  revisits  are  performed  within  a  session     Measuring  browsing  behavior  can  lead  to  incorrect  conclusions.     [1]  E.  Herder.  Characteriza*ons  of  user  web  revisit  behavior.  In  LWA,  2005.  
  • 7. Characteris%cs   of  online   mul%tasking   Danboard's  Messy  Home  by  Mullenkedheim  
  • 8. DATA SET Interac%on  data   •  July  2012   •  2.5M  users   •  785M  page  views     •  We  defined  a  new  naviga2on  model                                             (see  paper  for  detail)       •  Categoriza2on  of  the  most  frequent  accessed  sites   (e.g.  mail,  news,  shopping)   »  11  categories  (news),  33  subcategories  (e.g.  news   finance,  news  society)   »  760  sites  from  70  countries/regions       8  JaneGe  Lehmann   Characteris2cs  
  • 9. Visit activity Visit  frequency     9  JaneGe  Lehmann   Characteris2cs   Mul%tasking  depends  on  the  site  under   considera%on     •  Social  media  sites  are  revisited  the   most   •  News  (tech)  sites  are  the  least     revisited  sites   news (finance) news (tech) social media mail 2.09 1.76 2.28 2.09 4.65 1.59 4.78 4.61 #Visits (avg sd)
  • 10. Visit activity Ac%vity  between  visits       10  JaneGe  Lehmann   Characteris2cs   Differences  in  the  absence  %me     •  50%  of  sites  are  revisited  aCer  less   than  1min            -­‐  Interrup*on  of  a  task   •  There  are  revisits  aCer  a  long  break               -­‐  Returning  to  a  site  to  perform  a  new   task   0.00 0.25 0.50 0.75 1.00 10 2 10 1 100 101 102 mail social media news (finance) news (tech) Cumulativeprobability Absence time [min] *   v2  v1   *   v3   *  -­‐  absence  2me  
  • 11. Visit activity Ac%vity  paLern       11  JaneGe  Lehmann   Characteris2cs   •  Four  types  of  "aGen2on  shiCs”   •  Complex  cases  refer  to  no   specific  paGern  or  repeated   paGern   •  Successive  visits  can  belong   together  (i.e.,  to  the  same  task)   0.23 0.28 0.33 mail sites news (finance) sites news (tech) sites social media sites decreasing attention increasing attention constant attention complex attention Proportionoftotal dwelltimeonsite p-value = 0.09 m = -0.01 p-value = 0.07 m = -0.02 p-value = 0.79 m = 0.00 0.23 0.28 0.33 Proportionoftotal dwelltimeonsite
  • 12. Danboard  by  sⓘndy°   Measuring     online   mul%tasking    
  • 13. Cumulative activity Cumula%ve  ac%vity                            vi  Browsing  ac2vity  during  the  ith  visit                    ivi  Browsing  ac2vity  between  the  (i-­‐1)th  and  ith  visit                    k=3  Rescaling  factor  for  ivi                    m  Browsing  ac2vity  (e.g.  dwell  2me,  page  views)     Assump%on:   If  users  return  aCer  short  2me,  they  return  to  con2nue  with  same  task.   If  users  return  aCer  longer  2me,  they  return  to  perform  a  new  task  -­‐  an  indica2on  of  loyalty  to   the  site.     13  JaneGe  Lehmann   Metrics   CumActm,k = log10 (v1 + ivi k •vi i=2 n ∑ ) iv2   v2  v1   iv3   v3  
  • 14. Cumulative activity Cumula%ve  ac%vity                            vi  Browsing  ac2vity  during  the  ith  visit                    ivi  Browsing  ac2vity  between  the  (i-­‐1)th  and  ith  visit                    k=3  Rescaling  factor  for  ivi                    m  Browsing  ac2vity  (e.g.  dwell  2me,  page  views)     Assump%on:   If  users  return  aCer  short  2me,  they  return  to  con2nue  with  same  task.   If  users  return  aCer  longer  2me,  they  return  to  perform  a  new  task  -­‐  an  indica2on  of  loyalty  to   the  site.     14  JaneGe  Lehmann   Metrics   CumActm,k = log10 (v1 + ivi k •vi i=2 n ∑ ) iv2   v2  v1   iv3   v3  
  • 15. Cumulative activity Cumula%ve  ac%vity                            vi  Browsing  ac2vity  during  the  ith  visit                    ivi  Browsing  ac2vity  between  the  (i-­‐1)th  and  ith  visit                    k=3  Rescaling  factor  for  ivi                    m  Browsing  ac2vity  (e.g.  dwell  2me,  page  views)     Assump%on:   If  users  return  aCer  short  2me,  they  return  to  con2nue  with  same  task.   If  users  return  aCer  longer  2me,  they  return  to  perform  a  new  task  -­‐  an  indica2on  of  loyalty  to   the  site.     15  JaneGe  Lehmann   Metrics   CumActm,k = log10 (v1 + ivi k •vi i=2 n ∑ ) iv2   v2  v1   iv3   v3   v1  +  v2  +  v3      
  • 16. Cumulative activity Cumula%ve  ac%vity                            vi  Browsing  ac2vity  during  the  ith  visit                    ivi  Browsing  ac2vity  between  the  (i-­‐1)th  and  ith  visit                    k=3  Rescaling  factor  for  ivi                    m  Browsing  ac2vity  (e.g.  dwell  2me,  page  views)     Assump%on:   If  users  return  aCer  short  2me,  they  return  to  con2nue  with  same  task.   If  users  return  aCer  longer  2me,  they  return  to  perform  a  new  task  -­‐  an  indica2on  of  loyalty  to   the  site.     16  JaneGe  Lehmann   Metrics   CumActm,k = log10 (v1 + ivi k •vi i=2 n ∑ ) iv2   v2  v1   iv3   v3  
  • 17. Cumulative activity Cumula%ve  ac%vity                            vi  Browsing  ac2vity  during  the  ith  visit                    ivi  Browsing  ac2vity  between  the  (i-­‐1)th  and  ith  visit                    k=3  Rescaling  factor  for  ivi                    m  Browsing  ac2vity  (e.g.  dwell  2me,  page  views)     Assump%on:   If  users  return  aCer  short  2me,  they  return  to  con2nue  with  same  task.   If  users  return  aCer  longer  2me,  they  return  to  perform  a  new  task  -­‐  an  indica2on  of  loyalty  to   the  site.     17  JaneGe  Lehmann   Metrics   CumActm,k = log10 (v1 + ivi k •vi i=2 n ∑ ) iv2   v2  v1   iv3   v3   v1  +  (iv2)3Ÿ  v2  +  (iv3)3Ÿ  v3      
  • 18. Activity pattern ALen%on  shiN  and  range                            n=4    Number  of  visits  in  session                      σ  Variance  in  the  visit  ac2vity                    μ  Average  of  the  visit  ac2vity                    inv  Modifica2on  of  the  “Inversion  number”         Descrip%on:   AGShiC  models  the  shiC  of  aGen2on  in  the  browsing  ac2vity   AGRange  describes  fluctua2ons  in  the  browsing  ac2vity     18  JaneGe  Lehmann   Metrics   AttShiftm,n = invm,n − minInvm,n | maxInvm,n |− | minInvm,n | AttRangem,n = σ (Vm,n ) µ(Vm,n )
  • 19. Activity pattern ALen%on  shiN  and  range   19  JaneGe  Lehmann   Metrics   -­‐1   0   1   0   constant   constant   constant   >  0   decreasing   complex   increasing   AUen*on  shiV   AUen*on  range  
  • 20. Comparing  the  ranking  of  the  sites   •  Visitdt  –  Dwell  2me  during  a  visit   •  Sessiondt  –  Dwell  2me  during  a  session                     Ø  Visitdt  and  Sessiondt  correlate   Ø  Otherwise  no  correla2on  à  the  other  metrics  capture  different  aspects  of   browsing  behavior   Comparing metrics 20  JaneGe  Lehmann   Metrics   Visitdt   Sessiondt   CumActdt   ALShiNdt   Sessiondt   0.57   CumActdt   -­‐0.04   0.24   ALShiNdt   0.09   0.22   0.02   ALRangedt   -­‐0.01   -­‐0.01   -­‐0.26   0.19  
  • 21. “Models”  of  browsing  behavior   •  Clustering  of  sites  using  mul2tasking  and  standard  engagement  metrics:   •  CumActdt,  AGShiCdt,  AGRangedt   •  Visitdt,  Sessiondt     •  We  iden2fied  five  cluster:                 Models of browsing behavior 21  JaneGe  Lehmann   Metrics   C4: 74 sites 0.25 -0.25 0.75 -0.75 C5: 166 sites 0.25 -0.25 0.75 -0.75 C3: 156 sites 0.25 -0.25 0.75 -0.75 C2: 108 sites 0.25 -0.25 0.75 -0.75 C1: 172 sites 0.25 -0.25 0.75 -0.75 Visitdt [min] CumActdt,3 AttShiftdt,4 AttRangedt,4 Sessiondt [min]
  • 22. Models of browsing behavior 22  JaneGe  Lehmann   Metrics   Visitdt [min] CumActdt,3 AttShiftdt,4 AttRangedt,4 Sessiondt [min] C2: 108 sites auctions, front page, shopping, dating 0.25 -0.25 0.75 -0.75 C1: 172 sites mail, maps, news, news (soc.) 0.25 -0.25 0.75 -0.75 One  task  during  a  session     §  High  dwell  2me  per  visit  and  during   the  whole  session     §  Users  return  to  con2nue  a  task  (short   absence  2me)     §  C1:  aGen2on  is  shiCing  to  another  site   §  C2:  aGen2on  is  shiCing  slowly  towards   the  site  
  • 23. C4: 74 sites front page, search, download C3: 156 sites auctions, search, front page, shopping 0.25 -0.25 0.75 -0.75 0.25 -0.25 0.75 -0.75 Models of browsing behavior 23  JaneGe  Lehmann   Metrics   Several  tasks  during  a  session     §  Users  perform  several  tasks  on  these   sites  during  a  session   §  No  simple  ac2vity  paGern     §  C3:  Dwell  2me  per  visit  is  low,  but  the   dwell  2me  per  session  is  high     Visitdt [min] CumActdt,3 AttShiftdt,4 AttRangedt,4 Sessiondt [min]
  • 24. C5: 166 sites service, download, blogging, news (soc.) 0.25 -0.25 0.75 -0.75 Models of browsing behavior 24  JaneGe  Lehmann   Metrics   Sites  with  low  ac%vity     §  Users  do  not  spend  a  lot  of  2me  on   these  sites     §  Time  between  visits  is  short     §  AGen2on  is  shiCing  towards  the  site   Visitdt [min] CumActdt,3 AttShiftdt,4 AttRangedt,4 Sessiondt [min]
  • 25. C2: 108 sites auctions, front page, shopping, dating 0.25 -0.25 0.75 -0.75 C3: 156 sites auctions, search, front page, shopping 0.25 -0.25 0.75 -0.75 Models of browsing behavior 25  JaneGe  Lehmann   Metrics   Browsing  behavior  can  differ  between   sites  of  the  same  category     §  C2:  users  visit  site  once  to  perform   their  task   §  C3:  users  visit  site  several  2mes  to   perform  task(s)   Visitdt [min] CumActdt,3 AttShiftdt,4 AttRangedt,4 Sessiondt [min]
  • 26. SUMMARY and Future Work JaneGe  Lehmann   26   •  Online  mul2tasking  affects  the  way  users  access  sites  –  Standard  metrics   do  not  capture  this!!!   •  We  defined  metrics  that  describe  different  aspects  of  mul2tasking   •  CumAct  accounts  for  the  2me  between  visits   •  AGShiC,  AGRange  describe  aGen2on  shiCs   •  We  showed  that  mul2tasking  depends  on  the  site  under  considera2on     Future  work:   •  Can  we  improve  the  defini2on  of  a  task?   •  How  does  mul2tasking  affect  other  metrics,  such  as  bounce  rate  and  click-­‐ through  rate?   •  Does  mul2tasking  differ  in  different  countries?   Summary  
  • 27. Janette Lehmann Universitat Pompeu Fabra, Spain lehmannj@acm.org Mounia Lalmas Yahoo Labs London mounia@acm.org George Dupret Yahoo Labs Sunnyvale gdupret@yahoo-inc.com Ricardo Baeza-Yates Yahoo Labs Barcelona rbaeza@acm.org Online Multitasking + User Engagement