Semantic Networks of Interests in Online NSSI Communities

Semantic Networks of Interests in Online NSSICommunities

A Case Study in Mining Pathological Adolescent Minds

Dmitry Zinoviev, Dan Stefanescu, Lance Swenson, Gary Fireman

Mathematics and Computer Science Department, Psychology DepartmentSuffolk University, Boston

June 22, 2012

What Is NSSI?

Non-suicidal self-injury (NSSI) is the direct, deliberate destruction ofone’s own body tissue in the absence of suicidal intent.

It is practiced primarily by adolescents and young adults and is oftenconcealed from others.

Common NSSI activities include skin cutting, banging or hittingoneself, and burns.

Dmitry Zinoviev, Dan Stefanescu, Lance Swenson, Gary Fireman Semantic Networks of Interests in Online NSSI Communities

Why NSSI Matters?

14% to 21% of adolescents and 17% to 25% of young adults haveengaged in NSSI at some point in their lives

NSSI is repeatedly found to be associated with significant emotionaland behavioral dysfunction (e.g., eating disorders, suicide).

Can we identify NSSI persons by automatically analyzing secondarydata publicly available from massive online social networks (MOSN)?


NSSI and Massive Online Social Networks

Many popular MOSNs (e.g., Facebook and LiveJournal) allow usersto declare their interests.

Associations between interest lists and NSSI community membershipsuggest that “likes” or interest lists may be serving as identitysignals.

Profile page with interests of a random NSSI LiveJournal user:


NSSI Communities in LiveJournal

LiveJournal[.com] is a popular massive online blogging socialnetwork site (BSN)—a bimodal venue where users engage in bothpublishing and social activities.

Bloggers can form contact lists, subscribe to their friends’ blogs,comment on selected blog posts, declare interests, and participate incommunities—collective blogs.

32 mln individual and community accounts.

43 identified NSSI-related communities (some of them promoteNSSI activities, while others advocate for NSSI abstinence) with22,000 members and/or posters.

The total number of harvested interests is ∼150,000, includingmisspelled, abbreviated, and hyphenated variants.


Users and Interests

M is the adjacency matrix of a two-mode network of the NSSI users andtheir interests:

Mij =

{1 iff user Ui declares interest Vj

0 else

Should we use Pearson correlation (a.k.a. cosine distance) to calculatesimilarities of interests?Yes—if all users were different. However, a thematic community is likelyto be more homogeneous than a random group of users.


Kovacz Correlations

B. Kovacz: “Two terms are similar with correlation Θij ∈ [−1, 1] if theyare used by similar people; two people are similar with correlationΦij ∈ [−1, 1] if they use similar terms.” (We calculate Φ but do not useit.)

Initialize:

Mi = Mi,? −Mi,?

M j = M?,j −M?,j

Θij,0 = Φij,0 = δij

Iterate:

Θij,k+1 = MiΦkMTj /√(

MiΦkMTi

)(M jΦkM jT )

Φij,k+1 = MTi ΘkMj/

√(MT

i ΘkMi

)(M jTΘkM j)


From Θ to an Adjacency Matrix

Θ is a dense symmetric signed square matrix with few or no zeroterms.

The distribution of Θij is close to uniform.

Restricted to the top 600 most often declared interests (due tocomputational resources).

Convert Θ to a sparse adjacency matrix Ψ:

Ψij =

{Θij if Θij ≥ 0.8

0 else


Semantic Map, as Seen by Gephi


Clustering

Four major clusters: “music” (MUS), “pathology” (PAT), “dailylife/emotions” (DLE), and “creativity” (CRE). Examples of terms:

MUS: atreyu, him, incubus, korn, my chemical romance, nirvana,rancid, system of a down, the perfect circle;

PAT: alcohol, anorexia, bulimia, burning, cutting, handcuffs,pain, self-injury and self-mutilation (both with andwithout the dash), spikes, weeds;

DLE: cameras, cloths, dvds, flirting, flowers, fun, quotes,smiling, hearts (also as an HTML entity &hearts; and as♥);

CRE: astrology, books, languages, philosophy, psychology,shakespeare, sociology, travel, wine.


Bridges

The border zones are spanned with few bridge interests:

PAT/MUS: (black) eyeliner, girl interrupted, metal;

PAT/DLE: candy, girls, insomnia, red, rock music, sex;

MUS/DLE: animals, camping, fashion, games, honesty, humor,travel(l)ing;

All clusters: bands, bracelets, hoodies, lesbians, making out.

Can these bridges be viewed as NSSI “beacons?”


Pathology Cluster

The blowout of the PAT cluster:

(A link between two interests means many people often mention theseinterests together.)


Are the Links NSSI Specific?

Compare the NSSI communities to a random sample of LiveJournalusers—doesn’t work, they share very few interests!

Find appropriate communities that may share interests with theNSSI population:

SMM “sexy-mood-music” (6,700 members, average age 25years, music),

M15M “movies-in-fifteen-minutes” (13,300 members,average age 28 years, video fans).

Calculate the intersection between the NSSI semantic network andeach of the other semantic networks under consideration. Theintersection contains the associations that are significant for bothcommunities and presumably are pathology-free.


Pathology-Free Networks

Intersection of the NSSI and SMM networks:

Well-defined CRE and DLE clusters. The MUS cluster is sparse andsubdivided into movies and proper music. The PAT cluster is gone!


Leisure on Their Mind

Our findings appear indicative of the growing global middle-class youthculture revolving around leisure activities (e.g., music, art) reflectingadolescent development in internationally-connected networks. This issupported by the similarities between the NSSI interested communitiesand the non-pathological comparison communities.


Conclusions

We constructed a semantic network of interests declared bynon-suicidal self-injury (NSSI) bloggers of LiveJournal.

The network consists of four clearly separated interest clusterscorresponding to the pathological terms, daily life, popular music,and creativity.

The interests that bridge gaps between the pathology cluster andthe other three clusters can be used as beacons signaling thepotential presence of an NSSI behavior.

The extent of MOSN NSSI-related communities on LiveJournalcould evidence the limited opportunities for social networking amongpeople.


Thanks!

This research has been supported in part by the College of Arts andSciences, Suffolk University, through an undergraduate researchassistantship grant.The authors are grateful to Zoe Wells of Suffolk University for preliminarydata collection and Dr. Jim Hollander and Prof. John Boyd forsuggestions on combining graphs.


Semantic Networks of Interests in Online NSSI Communities

Health & Medicine

Transcript of Semantic Networks of Interests in Online NSSI Communities