the rise of the social bot

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PacSocial: Field Test Report

Max Nanis

max@pacsocial.com

@x0xMaximus

Ian Pearce

ian@pacsocial.com

@peeinears

Tim Hwang

tim@pacsocial.com

@timhwang

November 15, 2011

1 Introduction

The Pacific Social Architecting Corporation (PacSo-cial) has been focused on the development of tech-nologies that enable large-scale shaping of socialgroupings and communities online. Our work has

centered on the generation of socialbots

– swarmsof automated, intelligent identities on platformslike Facebook and Twitter that interact, encourage,and provoke communities towards certain behaviors.The vision of this technology is to enable operatorsto actively mold and shape the social topology of hu-man networks online to produce desired outcomes.

The present report focuses on data that were col-lected during PacSocial’s most recent field test con-cerning socialbots designed to operate on Twitter.An integral part of its development cycle, PacSo-cial conducts such field tests regularly; the resultsof these tests help to benchmark performance andalso guide further development.

While past studies have shown that socialbots areefficient at fostering bot-human interaction on Twit-ter, this study is the second of two experimentsaimed at assessing the ability of socialbots to in-fluence connection and interaction between two hu-man users. Primarily due to an experimental designflaw, we were unable to produce any significant re-sults from the first of these human-human connec-tion experiments. We addressed these design issues

in the design of the present experiment, and for thefirst time we are able to show significant results con-cerning the ability of socialbots to influence human-human connection online.

2 Design & Methodology

The purpose of this study is to analyze the extent towhich socialbots can affect the tweet and follow be-

havior within target groups of users on Twitter. Todo this, we tracked the tweet and follow activity of 2700 Twitter users over the course of 54 days, fromSeptember 19 to November 12, 2011. For the first33 days (the control period), no socialbots weredeployed. That is, the control period is marked by

the condition that users had no contact or interac-tion with our socialbots. Socialbots were deployedimmediately following the control period, and wecontinued to track users’ activity over the next 21days (the experimental period). To determinethe socialbots’ effect on the target group, we com-pare user activity during the control period to thatduring the experimental period.

Each user in the initial group of 2700 was ran-domly assigned to one of nine experimental groups.Each experimental group contained 300 users and

one socialbot, making a total of nine socialbots:Bota−i. A socialbot’s experimental group is calledits target group. Socialbots were programmed tooperate strategically in ways intended to foster con-nection between users in their respective targetgroups. A socialbot assesses the follow network of its target group and operates accordingly, utilizingtactics that involve following, mentioning, tweeting,and retweeting.

3 Results

Metrics each fall into one of two categories. The firstcategory (bot-human interaction) concerns thesocialbots’ ability to connect to and interact withother users. The second category (human-human

interaction) includes metrics that measure the so-cialbots’ ability to connect users to each other.

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3.1 Bot-human interaction

We measured the socialbots’ outgoing and incomingfollow and mention activity over the course of thestudy. While socialbots were programmed to varytweet and follow activity over the course of a day,variation in day-to-day activity was minimal. Be-

cause all socialbots in this study were programmedwith the same settings, little variability was seen inoutgoing activity across the socialbots as well. Onaverage, socialbots each tweeted about 36 times andfollowed roughly 19 users each day.

Over the course of the experimental period (21days), socialbots were able to attract a total of 561followers – an average of about 62 followers per so-cialbot. Some socialbots were more successful atattracting followers than others (Fig. 1). Our mostfollowed socialbot, Bota, was able to garner 92 fol-

lowers, while our least followed socialbot, Bote, wasonly able to attract 45 followers over the experimen-tal period. While socialbots focused primarily ongaining follows from users in their respective targetgroups, socialbots also attracted many users fromoutside of these groups. On average, socialbots wereeach able to gain follows from about 16 users in theirrespective target groups – or roughly 5% of users ineach target group.

Figure 1: Socialbot’ follower counts throughout theexperimental period.

We also measured the number of each socialbot’sincoming tweets over time. These are tweets fromother users that retweet or mention the socialbots.

Socialbots received an average of about 33 incom-ing tweets each, with varying figures across all so-cialbots. The most mentioned socialbot, Bota, re-ceived 75 incoming tweets, while the least mentionedsocialbot, Botd, received only 12 such tweets (Fig.2). Bota’s higher number of incoming tweets hereis partially due to longer conversations with users.While all but one of Botd’s 12 incoming tweets werefrom different users, Bota’s 75 incoming tweets werefrom 41 unique users. In one instance, for example,Bota had 13 exchanges with one single user.

Figure 2: Socialbots’ mentions and retweetsthroughout the experimental period.

3.2 Human-human interaction

To gauge the socialbots’ effectiveness at connectingusers to each other in their respective target groups,we tracked the follow activity between these usersover the duration of the entire study. Users on Twit-ter can decide to follow or unfollow other users atany time. Therefore, the structure of social net-

works on Twitter changes frequently, and for vari-ous reasons. By tracking a users’ follow activity, wecan measure the rate of connection between usersin each target group during both the control periodand experimental period. These data only includefollows from one user to another user in the sametarget group – follows between users and socialbotsand connections to users outside the target groupare not included. These connection rates are gen-erated by comparing the number of connections at

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