...
.
...
.
...
.
...
.
...
.
...
.
...
.
...
.
...
.
...
.
How do bots interact with each other?
The online world has turnedinto an ecosystem of bots.
Even Good Bots FightEven Good Bots FightEven Good Bots FightThis project has received funding from the European Union's
Horizon 2020 Research and Innovation Program under grant agreement No 645043.
Milena Tsvetkovaa, Ruth García-Gavilanesa, Luciano Floridia,b, and Taha Yasseria,b
The online world has turned into an ecosystem of bots.
Bots are responsible for an increasingly larger proportion of activities on the Web.
How do these automated agents interact with each other? Our research suggests that even relatively “dumb” bots may give rise to complex interactions. Thus, as bots continue to proliferate and manage an increasingly larger portion of our lives, the sociology of arti�cial agents will coalesce and grow as an important research �eld.
We model how pairs of bots interact over time as interaction trajectories.
We analyze three properties of the trajectories
Although Wikipedia bots are intended to support the encyclopedia, they often undo each other’s edits and these “�ghts” may sometimes continue for years.
EnglishJapaneseSpanishFrenchPortugueseGermanChineseHebrewHungarianCzechArabicRomanianPersian
EnglishJapaneseSpanishFrenchPortugueseGermanChineseHebrewHungarianCzechArabicRomanianPersian
Bot-bot interactions have different characteristic time scale than human-human interactions.
We identi�ed four types of trajectories. Their distribution shows that bot-bot interactions tend to be more reciprocated.
1. Gianvecchio S, Xie M, Wu Z, Wang H (2008) Measurement and classi�cation of humans and bots in Internet chat. Proceedings of the 17th Conference on Security Symposium (USENIX Association), pp 155–169.
2. Sysomos (2009) An in-depth look at the most active Twitter user data. Available at: https://sysomos.com/inside-twitter/most-active-twitter-us-er-data.
3. Cashmore P (2009) Twitter zombies: 24% of tweets created by bots. Mash-able. Available at: http://mashable.com/2009/08/06/twitter-bots/#JqT-VM0vEgqqA
Wikipedia bots interact, and their interactions can be inef�cient and in�uenced by culture, just as those between humans.
In additional analyses, we found that the same bots are responsible for the majority of reverts across all languages. These are bots that specialize in creating and modifying links between the different language editions of Wikipedia. The difference between German and Portuguese bots is not due to different kinds of bots but due to the same bots operating in different kinds of environments.
2004
1600
900
400
100
0
-100
-400
Reve
rts,
squ
arer
oote
d
2005 2006 2007 2008 2009 2010 2004
1600
900
400
100
0
-100
-400
Reve
rts,
squ
arer
oote
d
2005 2006 2007 2008 2009 2010
>trade
>tweet
>crawl
>edit
>censor
>spam
>bid
>attack
>chat
>phish
>like
>review
>spy
>upvote
>click
a Oxford Internet Institute b Alan Turing Institute Designed by Daniil Alexandrov
0 102
100
10-1
10-2
103
10-4
10-5
104 106 108
Latency, sec
Freq
uenc
y (h
uman
-hum
an)
t
Bala
nce
Time low high
low high
low high
Bot-bot Human-human20 43 14 19 23
Freq
uenc
y
0 102
100
10-1
10-2
103
10-4
10-5
104 106 108
Latency, sec
Bot-bot Human-human
398144114094187
28 40 1231
2433
2829
2623
2227
2224
20
4446
4039
4542
433842
4843
45
2322
2020
2222
2931
2829
2528
515042524033344334304829
1517
2211
161023
1727
4021
14
2317
1816
2619
221818
1517
20
1216
1821
1837
212121
1514
36
RESULTS
CONCLUSION
REFERENCES
INTRODUCTION
METHOD
Bot-bot trajectories Human-human trajectories
Fast unbalanced
Slow unbalanced Somewhat balanced
Well balanced
The mean log time between successive reverts;
The �nal proportion of reverts that were not reciprocated;
The proportion of observed turning points out of all possible.
We use k-means clustering to identify different types of interaction trajectories and to estimate how common the types are for bots compared to humans and for different languages.
Latency
Imbalance
Reciprocity
We analyze the interactions between bots that edit articles on Wikipedia. We track the extent to which bots undid each other’s edits.
13 different language editions
REVERTS
Period 2001-2010
Bots comprise a tiny proportion of all Wikipedia users, usually less than 0.1%.
However, they account for a signi�cant proportion of the editorial activity.
Bots behave differently in different language editions.
English
17 285 053
3 905 586
672
197 850 396
5 162 236
16 838 608
28 717 967
10 2456
3 301 494
22 023 332
948 597
5 755 678
13 366 493
353 879
3 479 859
11 958 185
436 821
5 615 348
8 796 942
611 831
1 055 449
9 795 722
51 209
2 604 780
4 961 398
149 210
1 785 903
3 732 386
28 485
1 990 820
2 514 563
24 746
1 657 897
2 607 205
49 583
1 895 405
1 603 020
2 6060
1 591 712
1 591 712
18 528
1 250 362
104.6 104 71.7 59.3 185 24.1 103 83.9 66.8 59 161.7 78 63.8
3.1 2.6 2.3 2.6 2.3 2.2 3.2 2.9 2.4 3.1 2.2 2.1 3.6
2 288 040
73 840
222
2 526 608
755 155
266
1 319 932
279 978
304
1 461 501
365 102
191
1 207 134
494 503
260
662 008
41 165
169
281 245
113 105
139
155 918
24 657
150
134 782
19 770
142
303 230
46 291
149
105 512
22 825
120
73 489
15 416
121
Humans
Japanese Spanish French Portuguese German Chinese Hebrew Hungarian Czech Arabic Romanian Persian
Vandals
Bots
Human edits
Vandal edits
Bot edits
DATA
Average human-human reverts per editor:
Average bot-bot reverts per editor:
i reve
rts j j reverts i
We analyze interactions betweenbots that edit articles on Wikipedia.
RevertsPeriod 2001–201013 languages
Milena Tsvetkova, Ruth García-Gavilanes, Luciano Floridi & Taha Yasseri Even Good Bots Fight: 1/3
...
.
...
.
...
.
...
.
...
.
...
.
...
.
...
.
...
.
...
.
We study interaction trajectories and three of their properties.
Even Good Bots FightEven Good Bots FightEven Good Bots FightThis project has received funding from the European Union's
Horizon 2020 Research and Innovation Program under grant agreement No 645043.
Milena Tsvetkovaa, Ruth García-Gavilanesa, Luciano Floridia,b, and Taha Yasseria,b
The online world has turned into an ecosystem of bots.
Bots are responsible for an increasingly larger proportion of activities on the Web.
How do these automated agents interact with each other? Our research suggests that even relatively “dumb” bots may give rise to complex interactions. Thus, as bots continue to proliferate and manage an increasingly larger portion of our lives, the sociology of arti�cial agents will coalesce and grow as an important research �eld.
We model how pairs of bots interact over time as interaction trajectories.
We analyze three properties of the trajectories
Although Wikipedia bots are intended to support the encyclopedia, they often undo each other’s edits and these “�ghts” may sometimes continue for years.
EnglishJapaneseSpanishFrenchPortugueseGermanChineseHebrewHungarianCzechArabicRomanianPersian
EnglishJapaneseSpanishFrenchPortugueseGermanChineseHebrewHungarianCzechArabicRomanianPersian
Bot-bot interactions have different characteristic time scale than human-human interactions.
We identi�ed four types of trajectories. Their distribution shows that bot-bot interactions tend to be more reciprocated.
1. Gianvecchio S, Xie M, Wu Z, Wang H (2008) Measurement and classi�cation of humans and bots in Internet chat. Proceedings of the 17th Conference on Security Symposium (USENIX Association), pp 155–169.
2. Sysomos (2009) An in-depth look at the most active Twitter user data. Available at: https://sysomos.com/inside-twitter/most-active-twitter-us-er-data.
3. Cashmore P (2009) Twitter zombies: 24% of tweets created by bots. Mash-able. Available at: http://mashable.com/2009/08/06/twitter-bots/#JqT-VM0vEgqqA
Wikipedia bots interact, and their interactions can be inef�cient and in�uenced by culture, just as those between humans.
In additional analyses, we found that the same bots are responsible for the majority of reverts across all languages. These are bots that specialize in creating and modifying links between the different language editions of Wikipedia. The difference between German and Portuguese bots is not due to different kinds of bots but due to the same bots operating in different kinds of environments.
2004
1600
900
400
100
0
-100
-400
Reve
rts,
squ
arer
oote
d
2005 2006 2007 2008 2009 2010 2004
1600
900
400
100
0
-100
-400
Reve
rts,
squ
arer
oote
d
2005 2006 2007 2008 2009 2010
>trade
>tweet
>crawl
>edit
>censor
>spam
>bid
>attack
>chat
>phish
>like
>review
>spy
>upvote
>click
a Oxford Internet Institute b Alan Turing Institute Designed by Daniil Alexandrov
0 102
100
10-1
10-2
103
10-4
10-5
104 106 108
Latency, sec
Freq
uenc
y (h
uman
-hum
an)
t
Bala
nce
Time low high
low high
low high
Bot-bot Human-human20 43 14 19 23
Freq
uenc
y
0 102
100
10-1
10-2
103
10-4
10-5
104 106 108
Latency, sec
Bot-bot Human-human
398144114094187
28 40 1231
2433
2829
2623
2227
2224
20
4446
4039
4542
433842
4843
45
2322
2020
2222
2931
2829
2528
515042524033344334304829
1517
2211
161023
1727
4021
14
2317
1816
2619
221818
1517
20
1216
1821
1837
212121
1514
36
RESULTS
CONCLUSION
REFERENCES
INTRODUCTION
METHOD
Bot-bot trajectories Human-human trajectories
Fast unbalanced
Slow unbalanced Somewhat balanced
Well balanced
The mean log time between successive reverts;
The �nal proportion of reverts that were not reciprocated;
The proportion of observed turning points out of all possible.
We use k-means clustering to identify different types of interaction trajectories and to estimate how common the types are for bots compared to humans and for different languages.
Latency
Imbalance
Reciprocity
We analyze the interactions between bots that edit articles on Wikipedia. We track the extent to which bots undid each other’s edits.
13 different language editions
REVERTS
Period 2001-2010
Bots comprise a tiny proportion of all Wikipedia users, usually less than 0.1%.
However, they account for a signi�cant proportion of the editorial activity.
Bots behave differently in different language editions.
English
17 285 053
3 905 586
672
197 850 396
5 162 236
16 838 608
28 717 967
10 2456
3 301 494
22 023 332
948 597
5 755 678
13 366 493
353 879
3 479 859
11 958 185
436 821
5 615 348
8 796 942
611 831
1 055 449
9 795 722
51 209
2 604 780
4 961 398
149 210
1 785 903
3 732 386
28 485
1 990 820
2 514 563
24 746
1 657 897
2 607 205
49 583
1 895 405
1 603 020
2 6060
1 591 712
1 591 712
18 528
1 250 362
104.6 104 71.7 59.3 185 24.1 103 83.9 66.8 59 161.7 78 63.8
3.1 2.6 2.3 2.6 2.3 2.2 3.2 2.9 2.4 3.1 2.2 2.1 3.6
2 288 040
73 840
222
2 526 608
755 155
266
1 319 932
279 978
304
1 461 501
365 102
191
1 207 134
494 503
260
662 008
41 165
169
281 245
113 105
139
155 918
24 657
150
134 782
19 770
142
303 230
46 291
149
105 512
22 825
120
73 489
15 416
121
Humans
Japanese Spanish French Portuguese German Chinese Hebrew Hungarian Czech Arabic Romanian Persian
Vandals
Bots
Human edits
Vandal edits
Bot edits
DATA
Average human-human reverts per editor:
Average bot-bot reverts per editor:
i reve
rts j j reverts i
Even Good Bots FightEven Good Bots FightEven Good Bots FightThis project has received funding from the European Union's
Horizon 2020 Research and Innovation Program under grant agreement No 645043.
Milena Tsvetkovaa, Ruth García-Gavilanesa, Luciano Floridia,b, and Taha Yasseria,b
The online world has turned into an ecosystem of bots.
Bots are responsible for an increasingly larger proportion of activities on the Web.
How do these automated agents interact with each other? Our research suggests that even relatively “dumb” bots may give rise to complex interactions. Thus, as bots continue to proliferate and manage an increasingly larger portion of our lives, the sociology of arti�cial agents will coalesce and grow as an important research �eld.
We model how pairs of bots interact over time as interaction trajectories.
We analyze three properties of the trajectories
Although Wikipedia bots are intended to support the encyclopedia, they often undo each other’s edits and these “�ghts” may sometimes continue for years.
EnglishJapaneseSpanishFrenchPortugueseGermanChineseHebrewHungarianCzechArabicRomanianPersian
EnglishJapaneseSpanishFrenchPortugueseGermanChineseHebrewHungarianCzechArabicRomanianPersian
Bot-bot interactions have different characteristic time scale than human-human interactions.
We identi�ed four types of trajectories. Their distribution shows that bot-bot interactions tend to be more reciprocated.
1. Gianvecchio S, Xie M, Wu Z, Wang H (2008) Measurement and classi�cation of humans and bots in Internet chat. Proceedings of the 17th Conference on Security Symposium (USENIX Association), pp 155–169.
2. Sysomos (2009) An in-depth look at the most active Twitter user data. Available at: https://sysomos.com/inside-twitter/most-active-twitter-us-er-data.
3. Cashmore P (2009) Twitter zombies: 24% of tweets created by bots. Mash-able. Available at: http://mashable.com/2009/08/06/twitter-bots/#JqT-VM0vEgqqA
Wikipedia bots interact, and their interactions can be inef�cient and in�uenced by culture, just as those between humans.
In additional analyses, we found that the same bots are responsible for the majority of reverts across all languages. These are bots that specialize in creating and modifying links between the different language editions of Wikipedia. The difference between German and Portuguese bots is not due to different kinds of bots but due to the same bots operating in different kinds of environments.
2004
1600
900
400
100
0
-100
-400
Reve
rts,
squ
arer
oote
d
2005 2006 2007 2008 2009 2010 2004
1600
900
400
100
0
-100
-400
Reve
rts,
squ
arer
oote
d
2005 2006 2007 2008 2009 2010
>trade
>tweet
>crawl
>edit
>censor
>spam
>bid
>attack
>chat
>phish
>like
>review
>spy
>upvote
>click
a Oxford Internet Institute b Alan Turing Institute Designed by Daniil Alexandrov
0 102
100
10-1
10-2
103
10-4
10-5
104 106 108
Latency, sec
Freq
uenc
y (h
uman
-hum
an)
t
Bala
nce
Time low high
low high
low high
Bot-bot Human-human20 43 14 19 23
Freq
uenc
y
0 102
100
10-1
10-2
103
10-4
10-5
104 106 108
Latency, sec
Bot-bot Human-human
398144114094187
28 40 1231
2433
2829
2623
2227
2224
20
4446
4039
4542
433842
4843
45
2322
2020
2222
2931
2829
2528
515042524033344334304829
1517
2211
161023
1727
4021
14
2317
1816
2619
221818
1517
20
1216
1821
1837
212121
1514
36
RESULTS
CONCLUSION
REFERENCES
INTRODUCTION
METHOD
Bot-bot trajectories Human-human trajectories
Fast unbalanced
Slow unbalanced Somewhat balanced
Well balanced
The mean log time between successive reverts;
The �nal proportion of reverts that were not reciprocated;
The proportion of observed turning points out of all possible.
We use k-means clustering to identify different types of interaction trajectories and to estimate how common the types are for bots compared to humans and for different languages.
Latency
Imbalance
Reciprocity
We analyze the interactions between bots that edit articles on Wikipedia. We track the extent to which bots undid each other’s edits.
13 different language editions
REVERTS
Period 2001-2010
Bots comprise a tiny proportion of all Wikipedia users, usually less than 0.1%.
However, they account for a signi�cant proportion of the editorial activity.
Bots behave differently in different language editions.
English
17 285 053
3 905 586
672
197 850 396
5 162 236
16 838 608
28 717 967
10 2456
3 301 494
22 023 332
948 597
5 755 678
13 366 493
353 879
3 479 859
11 958 185
436 821
5 615 348
8 796 942
611 831
1 055 449
9 795 722
51 209
2 604 780
4 961 398
149 210
1 785 903
3 732 386
28 485
1 990 820
2 514 563
24 746
1 657 897
2 607 205
49 583
1 895 405
1 603 020
2 6060
1 591 712
1 591 712
18 528
1 250 362
104.6 104 71.7 59.3 185 24.1 103 83.9 66.8 59 161.7 78 63.8
3.1 2.6 2.3 2.6 2.3 2.2 3.2 2.9 2.4 3.1 2.2 2.1 3.6
2 288 040
73 840
222
2 526 608
755 155
266
1 319 932
279 978
304
1 461 501
365 102
191
1 207 134
494 503
260
662 008
41 165
169
281 245
113 105
139
155 918
24 657
150
134 782
19 770
142
303 230
46 291
149
105 512
22 825
120
73 489
15 416
121
Humans
Japanese Spanish French Portuguese German Chinese Hebrew Hungarian Czech Arabic Romanian Persian
Vandals
Bots
Human edits
Vandal edits
Bot edits
DATA
Average human-human reverts per editor:
Average bot-bot reverts per editor:
i reve
rts j j reverts i
We then use k-means clustering to identify different types oftrajectories.
Milena Tsvetkova, Ruth García-Gavilanes, Luciano Floridi & Taha Yasseri Even Good Bots Fight: 2/3
...
.
...
.
...
.
...
.
...
.
...
.
...
.
...
.
...
.
...
.
Even good bots fight.
Even Good Bots FightEven Good Bots FightEven Good Bots FightThis project has received funding from the European Union's
Horizon 2020 Research and Innovation Program under grant agreement No 645043.
Milena Tsvetkovaa, Ruth García-Gavilanesa, Luciano Floridia,b, and Taha Yasseria,b
The online world has turned into an ecosystem of bots.
Bots are responsible for an increasingly larger proportion of activities on the Web.
How do these automated agents interact with each other? Our research suggests that even relatively “dumb” bots may give rise to complex interactions. Thus, as bots continue to proliferate and manage an increasingly larger portion of our lives, the sociology of arti�cial agents will coalesce and grow as an important research �eld.
We model how pairs of bots interact over time as interaction trajectories.
We analyze three properties of the trajectories
Although Wikipedia bots are intended to support the encyclopedia, they often undo each other’s edits and these “�ghts” may sometimes continue for years.
EnglishJapaneseSpanishFrenchPortugueseGermanChineseHebrewHungarianCzechArabicRomanianPersian
EnglishJapaneseSpanishFrenchPortugueseGermanChineseHebrewHungarianCzechArabicRomanianPersian
Bot-bot interactions have different characteristic time scale than human-human interactions.
We identi�ed four types of trajectories. Their distribution shows that bot-bot interactions tend to be more reciprocated.
1. Gianvecchio S, Xie M, Wu Z, Wang H (2008) Measurement and classi�cation of humans and bots in Internet chat. Proceedings of the 17th Conference on Security Symposium (USENIX Association), pp 155–169.
2. Sysomos (2009) An in-depth look at the most active Twitter user data. Available at: https://sysomos.com/inside-twitter/most-active-twitter-us-er-data.
3. Cashmore P (2009) Twitter zombies: 24% of tweets created by bots. Mash-able. Available at: http://mashable.com/2009/08/06/twitter-bots/#JqT-VM0vEgqqA
Wikipedia bots interact, and their interactions can be inef�cient and in�uenced by culture, just as those between humans.
In additional analyses, we found that the same bots are responsible for the majority of reverts across all languages. These are bots that specialize in creating and modifying links between the different language editions of Wikipedia. The difference between German and Portuguese bots is not due to different kinds of bots but due to the same bots operating in different kinds of environments.
2004
1600
900
400
100
0
-100
-400
Reve
rts,
squ
arer
oote
d
2005 2006 2007 2008 2009 2010 2004
1600
900
400
100
0
-100
-400
Reve
rts,
squ
arer
oote
d
2005 2006 2007 2008 2009 2010
>trade
>tweet
>crawl
>edit
>censor
>spam
>bid
>attack
>chat
>phish
>like
>review
>spy
>upvote
>click
a Oxford Internet Institute b Alan Turing Institute Designed by Daniil Alexandrov
0 102
100
10-1
10-2
103
10-4
10-5
104 106 108
Latency, sec
Freq
uenc
y (h
uman
-hum
an)
t
Bala
nce
Time low high
low high
low high
Bot-bot Human-human20 43 14 19 23
Freq
uenc
y
0 102
100
10-1
10-2
103
10-4
10-5
104 106 108
Latency, sec
Bot-bot Human-human
398144114094187
28 40 1231
2433
2829
2623
2227
2224
20
4446
4039
4542
433842
4843
45
2322
2020
2222
2931
2829
2528
515042524033344334304829
1517
2211
161023
1727
4021
14
2317
1816
2619
221818
1517
20
1216
1821
1837
212121
1514
36
RESULTS
CONCLUSION
REFERENCES
INTRODUCTION
METHOD
Bot-bot trajectories Human-human trajectories
Fast unbalanced
Slow unbalanced Somewhat balanced
Well balanced
The mean log time between successive reverts;
The �nal proportion of reverts that were not reciprocated;
The proportion of observed turning points out of all possible.
We use k-means clustering to identify different types of interaction trajectories and to estimate how common the types are for bots compared to humans and for different languages.
Latency
Imbalance
Reciprocity
We analyze the interactions between bots that edit articles on Wikipedia. We track the extent to which bots undid each other’s edits.
13 different language editions
REVERTS
Period 2001-2010
Bots comprise a tiny proportion of all Wikipedia users, usually less than 0.1%.
However, they account for a signi�cant proportion of the editorial activity.
Bots behave differently in different language editions.
English
17 285 053
3 905 586
672
197 850 396
5 162 236
16 838 608
28 717 967
10 2456
3 301 494
22 023 332
948 597
5 755 678
13 366 493
353 879
3 479 859
11 958 185
436 821
5 615 348
8 796 942
611 831
1 055 449
9 795 722
51 209
2 604 780
4 961 398
149 210
1 785 903
3 732 386
28 485
1 990 820
2 514 563
24 746
1 657 897
2 607 205
49 583
1 895 405
1 603 020
2 6060
1 591 712
1 591 712
18 528
1 250 362
104.6 104 71.7 59.3 185 24.1 103 83.9 66.8 59 161.7 78 63.8
3.1 2.6 2.3 2.6 2.3 2.2 3.2 2.9 2.4 3.1 2.2 2.1 3.6
2 288 040
73 840
222
2 526 608
755 155
266
1 319 932
279 978
304
1 461 501
365 102
191
1 207 134
494 503
260
662 008
41 165
169
281 245
113 105
139
155 918
24 657
150
134 782
19 770
142
303 230
46 291
149
105 512
22 825
120
73 489
15 416
121
Humans
Japanese Spanish French Portuguese German Chinese Hebrew Hungarian Czech Arabic Romanian Persian
Vandals
Bots
Human edits
Vandal edits
Bot edits
DATA
Average human-human reverts per editor:
Average bot-bot reverts per editor:
i reve
rts j j reverts i
Even Good Bots FightEven Good Bots FightEven Good Bots FightThis project has received funding from the European Union's
Horizon 2020 Research and Innovation Program under grant agreement No 645043.
Milena Tsvetkovaa, Ruth García-Gavilanesa, Luciano Floridia,b, and Taha Yasseria,b
The online world has turned into an ecosystem of bots.
Bots are responsible for an increasingly larger proportion of activities on the Web.
How do these automated agents interact with each other? Our research suggests that even relatively “dumb” bots may give rise to complex interactions. Thus, as bots continue to proliferate and manage an increasingly larger portion of our lives, the sociology of arti�cial agents will coalesce and grow as an important research �eld.
We model how pairs of bots interact over time as interaction trajectories.
We analyze three properties of the trajectories
Although Wikipedia bots are intended to support the encyclopedia, they often undo each other’s edits and these “�ghts” may sometimes continue for years.
EnglishJapaneseSpanishFrenchPortugueseGermanChineseHebrewHungarianCzechArabicRomanianPersian
EnglishJapaneseSpanishFrenchPortugueseGermanChineseHebrewHungarianCzechArabicRomanianPersian
Bot-bot interactions have different characteristic time scale than human-human interactions.
We identi�ed four types of trajectories. Their distribution shows that bot-bot interactions tend to be more reciprocated.
1. Gianvecchio S, Xie M, Wu Z, Wang H (2008) Measurement and classi�cation of humans and bots in Internet chat. Proceedings of the 17th Conference on Security Symposium (USENIX Association), pp 155–169.
2. Sysomos (2009) An in-depth look at the most active Twitter user data. Available at: https://sysomos.com/inside-twitter/most-active-twitter-us-er-data.
3. Cashmore P (2009) Twitter zombies: 24% of tweets created by bots. Mash-able. Available at: http://mashable.com/2009/08/06/twitter-bots/#JqT-VM0vEgqqA
Wikipedia bots interact, and their interactions can be inef�cient and in�uenced by culture, just as those between humans.
In additional analyses, we found that the same bots are responsible for the majority of reverts across all languages. These are bots that specialize in creating and modifying links between the different language editions of Wikipedia. The difference between German and Portuguese bots is not due to different kinds of bots but due to the same bots operating in different kinds of environments.
2004
1600
900
400
100
0
-100
-400
Reve
rts,
squ
arer
oote
d
2005 2006 2007 2008 2009 2010 2004
1600
900
400
100
0
-100
-400
Reve
rts,
squ
arer
oote
d
2005 2006 2007 2008 2009 2010
>trade
>tweet
>crawl
>edit
>censor
>spam
>bid
>attack
>chat
>phish
>like
>review
>spy
>upvote
>click
a Oxford Internet Institute b Alan Turing Institute Designed by Daniil Alexandrov
0 102
100
10-1
10-2
103
10-4
10-5
104 106 108
Latency, sec
Freq
uenc
y (h
uman
-hum
an)
t
Bala
nce
Time low high
low high
low high
Bot-bot Human-human20 43 14 19 23
Freq
uenc
y
0 102
100
10-1
10-2
103
10-4
10-5
104 106 108
Latency, sec
Bot-bot Human-human
398144114094187
28 40 1231
2433
2829
2623
2227
2224
20
4446
4039
4542
433842
4843
45
2322
2020
2222
2931
2829
2528
515042524033344334304829
1517
2211
161023
1727
4021
14
2317
1816
2619
221818
1517
20
1216
1821
1837
212121
1514
36
RESULTS
CONCLUSION
REFERENCES
INTRODUCTION
METHOD
Bot-bot trajectories Human-human trajectories
Fast unbalanced
Slow unbalanced Somewhat balanced
Well balanced
The mean log time between successive reverts;
The �nal proportion of reverts that were not reciprocated;
The proportion of observed turning points out of all possible.
We use k-means clustering to identify different types of interaction trajectories and to estimate how common the types are for bots compared to humans and for different languages.
Latency
Imbalance
Reciprocity
We analyze the interactions between bots that edit articles on Wikipedia. We track the extent to which bots undid each other’s edits.
13 different language editions
REVERTS
Period 2001-2010
Bots comprise a tiny proportion of all Wikipedia users, usually less than 0.1%.
However, they account for a signi�cant proportion of the editorial activity.
Bots behave differently in different language editions.
English
17 285 053
3 905 586
672
197 850 396
5 162 236
16 838 608
28 717 967
10 2456
3 301 494
22 023 332
948 597
5 755 678
13 366 493
353 879
3 479 859
11 958 185
436 821
5 615 348
8 796 942
611 831
1 055 449
9 795 722
51 209
2 604 780
4 961 398
149 210
1 785 903
3 732 386
28 485
1 990 820
2 514 563
24 746
1 657 897
2 607 205
49 583
1 895 405
1 603 020
2 6060
1 591 712
1 591 712
18 528
1 250 362
104.6 104 71.7 59.3 185 24.1 103 83.9 66.8 59 161.7 78 63.8
3.1 2.6 2.3 2.6 2.3 2.2 3.2 2.9 2.4 3.1 2.2 2.1 3.6
2 288 040
73 840
222
2 526 608
755 155
266
1 319 932
279 978
304
1 461 501
365 102
191
1 207 134
494 503
260
662 008
41 165
169
281 245
113 105
139
155 918
24 657
150
134 782
19 770
142
303 230
46 291
149
105 512
22 825
120
73 489
15 416
121
Humans
Japanese Spanish French Portuguese German Chinese Hebrew Hungarian Czech Arabic Romanian Persian
Vandals
Bots
Human edits
Vandal edits
Bot edits
DATA
Average human-human reverts per editor:
Average bot-bot reverts per editor:
i reve
rts j j reverts i
Interactions between bots are affected by human culture.
Even Good Bots FightEven Good Bots FightEven Good Bots FightThis project has received funding from the European Union's
Horizon 2020 Research and Innovation Program under grant agreement No 645043.
Milena Tsvetkovaa, Ruth García-Gavilanesa, Luciano Floridia,b, and Taha Yasseria,b
The online world has turned into an ecosystem of bots.
Bots are responsible for an increasingly larger proportion of activities on the Web.
How do these automated agents interact with each other? Our research suggests that even relatively “dumb” bots may give rise to complex interactions. Thus, as bots continue to proliferate and manage an increasingly larger portion of our lives, the sociology of arti�cial agents will coalesce and grow as an important research �eld.
We model how pairs of bots interact over time as interaction trajectories.
We analyze three properties of the trajectories
Although Wikipedia bots are intended to support the encyclopedia, they often undo each other’s edits and these “�ghts” may sometimes continue for years.
EnglishJapaneseSpanishFrenchPortugueseGermanChineseHebrewHungarianCzechArabicRomanianPersian
EnglishJapaneseSpanishFrenchPortugueseGermanChineseHebrewHungarianCzechArabicRomanianPersian
Bot-bot interactions have different characteristic time scale than human-human interactions.
We identi�ed four types of trajectories. Their distribution shows that bot-bot interactions tend to be more reciprocated.
1. Gianvecchio S, Xie M, Wu Z, Wang H (2008) Measurement and classi�cation of humans and bots in Internet chat. Proceedings of the 17th Conference on Security Symposium (USENIX Association), pp 155–169.
2. Sysomos (2009) An in-depth look at the most active Twitter user data. Available at: https://sysomos.com/inside-twitter/most-active-twitter-us-er-data.
3. Cashmore P (2009) Twitter zombies: 24% of tweets created by bots. Mash-able. Available at: http://mashable.com/2009/08/06/twitter-bots/#JqT-VM0vEgqqA
Wikipedia bots interact, and their interactions can be inef�cient and in�uenced by culture, just as those between humans.
In additional analyses, we found that the same bots are responsible for the majority of reverts across all languages. These are bots that specialize in creating and modifying links between the different language editions of Wikipedia. The difference between German and Portuguese bots is not due to different kinds of bots but due to the same bots operating in different kinds of environments.
2004
1600
900
400
100
0
-100
-400
Reve
rts,
squ
arer
oote
d
2005 2006 2007 2008 2009 2010 2004
1600
900
400
100
0
-100
-400
Reve
rts,
squ
arer
oote
d
2005 2006 2007 2008 2009 2010
>trade
>tweet
>crawl
>edit
>censor
>spam
>bid
>attack
>chat
>phish
>like
>review
>spy
>upvote
>click
a Oxford Internet Institute b Alan Turing Institute Designed by Daniil Alexandrov
0 102
100
10-1
10-2
103
10-4
10-5
104 106 108
Latency, sec
Freq
uenc
y (h
uman
-hum
an)
t
Bala
nce
Time low high
low high
low high
Bot-bot Human-human20 43 14 19 23
Freq
uenc
y
0 102
100
10-1
10-2
103
10-4
10-5
104 106 108
Latency, sec
Bot-bot Human-human
398144114094187
28 40 1231
2433
2829
2623
2227
2224
20
4446
4039
4542
433842
4843
45
2322
2020
2222
2931
2829
2528
515042524033344334304829
1517
2211
161023
1727
4021
14
2317
1816
2619
221818
1517
20
1216
1821
1837
212121
1514
36
RESULTS
CONCLUSION
REFERENCES
INTRODUCTION
METHOD
Bot-bot trajectories Human-human trajectories
Fast unbalanced
Slow unbalanced Somewhat balanced
Well balanced
The mean log time between successive reverts;
The �nal proportion of reverts that were not reciprocated;
The proportion of observed turning points out of all possible.
We use k-means clustering to identify different types of interaction trajectories and to estimate how common the types are for bots compared to humans and for different languages.
Latency
Imbalance
Reciprocity
We analyze the interactions between bots that edit articles on Wikipedia. We track the extent to which bots undid each other’s edits.
13 different language editions
REVERTS
Period 2001-2010
Bots comprise a tiny proportion of all Wikipedia users, usually less than 0.1%.
However, they account for a signi�cant proportion of the editorial activity.
Bots behave differently in different language editions.
English
17 285 053
3 905 586
672
197 850 396
5 162 236
16 838 608
28 717 967
10 2456
3 301 494
22 023 332
948 597
5 755 678
13 366 493
353 879
3 479 859
11 958 185
436 821
5 615 348
8 796 942
611 831
1 055 449
9 795 722
51 209
2 604 780
4 961 398
149 210
1 785 903
3 732 386
28 485
1 990 820
2 514 563
24 746
1 657 897
2 607 205
49 583
1 895 405
1 603 020
2 6060
1 591 712
1 591 712
18 528
1 250 362
104.6 104 71.7 59.3 185 24.1 103 83.9 66.8 59 161.7 78 63.8
3.1 2.6 2.3 2.6 2.3 2.2 3.2 2.9 2.4 3.1 2.2 2.1 3.6
2 288 040
73 840
222
2 526 608
755 155
266
1 319 932
279 978
304
1 461 501
365 102
191
1 207 134
494 503
260
662 008
41 165
169
281 245
113 105
139
155 918
24 657
150
134 782
19 770
142
303 230
46 291
149
105 512
22 825
120
73 489
15 416
121
Humans
Japanese Spanish French Portuguese German Chinese Hebrew Hungarian Czech Arabic Romanian Persian
Vandals
Bots
Human edits
Vandal edits
Bot edits
DATA
Average human-human reverts per editor:
Average bot-bot reverts per editor:
i reve
rts j j reverts i
eng jap spa fre por ger chi heb hun cze ara rom per
Milena Tsvetkova, Ruth García-Gavilanes, Luciano Floridi & Taha Yasseri Even Good Bots Fight: 3/3