Analyzing Negative User Behavior in a Semi-anonymous Social Network

Abstract—Cyberbullying has emerged as an important andgrowing social problem, wherein people use online social networksand mobile phones to bully victims with offensive text, images,audio and video on a 24/7 basis. This paper studies negativeuser behavior in the Ask.fm social network, a popular new sitethat has led to many cases of cyberbullying, some leading tosuicidal behavior. We examine the occurrence of negative wordsin Ask.fm’s question/answer proﬁles along with the social networkof “likes” of questions/answers. We also examine properties ofusers with “cutting” behavior in this social network. I. I NTRODUCTION One of the most pressing problems in American highschools is bullying. However, with today’s technology, bullyingis moving beyond the schoolyards via cell phones, socialnetworks, online video and images, etc. As bad as ﬁghting andbullying was before the prevalence of personal technology, therecording and posting of hurtful content has made the impactof bullying much more harmful now. On average, 24% ofhigh school students have been the victim of cyberbullying[1]. Cyberbullying happens in many different ways, including:mean, negative and hurtful comments, pictures or videosposted online or on cell phones, or through the spread ofrumors or threats via technology.Although cyberbullying may not cause any physical dam-age initially, it has potentially devastating psychological effectslike depression, low self-esteem, suicide ideation, and evensuicide [2], [3]. For example, Phoebe Prince, a 15 year oldhigh school girl, committed suicide after being cyberbulliedby negative comments in the Facebook social network [4]. Hannah Smith, a 14 year old, hanged herself after negativecomments were posted on her Ask.fm page, a popular socialnetwork among teenagers [5]. Cyberbullying is such a seriousproblem that nine suicides have been linked with cyberbullyingon the Ask.fm Web site alone [6]. Cyberbullying was viewedas a contributing factor in the death of these teenagers [1]. Given the gravity of the problem and its rapid spread amongmiddle and high school students, there is an immediate andpressing need for research to understand how cyberbullyingoccurs today, so that techniques can be rapidly developed toaccurately detect, prevent, and mitigate cyberbullying.While most current studies have focused on the prevalenceand impact of cyberbullying in education and psychology [7]–[9], our interest is in understanding how social networks are be-ing used to enable cyberbullying. Prior work in cyberbullyinganalysis and detection in social networks has largely focusedon such social networks as Youtube, Formspring, MySpace,and Twitter [10]–[12]. [10] investigated both explicit andimplicit cyberbullying by analyzing negative text commentson Youtube and Formspring proﬁles. [11] investigates howintegration of MySpace user proﬁle information like genderin addition to text analysis can improve the accuracy ofcyberbullying detection in social networks [12] tries to detectbullying in Twitter text data by looking for inappropriatewords using a Naive Bayes classiﬁer. They track potentialbullies, their followers and the victims. All of these worksfocused on text-based analysis of negative words, and did notexploit social network relationships in their investigation ofcyberbullying.Our work seeks to understand whether social networkrelationship information can be useful in supplementing purelytext-based analysis in helping to identify negative user behaviorin social networks. We are interested in building graphsfrom social networks and analyzing their properties to extractfeatures such as in-degree or out-degree that may be useful inﬂagging such bad behavior.In particular, this paper chooses to focus on analyzing theAsk.fm social network for the following key reasons. First,Ask.fm is a major source of cyberbullying on the Internet. Infact, it ranks as the fourth worst site in terms of percentageof young users bullied according to a recent survey [13], afterFacebook, YouTube and Twitter. Second, very little researchhas been published to date concerning the Ask.fm social net-work. Third, Ask.fm is a highly popular and rapidly growingsocial network, with over 70 million registered users as ofAugust 2013 [14]. Finally, Ask.fm provides publicly accessibledata.One of our big challenges in analyzing the Ask.fm socialnetwork is that it behaves as a semi-anonymous social network.User proﬁles are public, but postings to each proﬁle by usersother than the owner are by default anonymous. In addition,we cannot obtain from the public proﬁles which users arefollowing which other users. As a result, it is not possible forus to construct a social graph based on friendships. However,we observe that another type of graph called an interactiongraph [15] can be extracted from the “likes” of comments. Weuse this insight to build and analyze interaction graphs thatembed social relationship information, to help identify negativeuser behavior in this semi-anonymous social network.