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Semantic Networks of Interests in Online NSSI Communities

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Information about Semantic Networks of Interests in Online NSSI Communities
Health & Medicine

Published on February 26, 2014

Author: DmitryZinoviev

Source: slideshare.net

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Semantic Networks of Interests in Online NSSI Communities A Case Study in Mining Pathological Adolescent Minds Dmitry Zinoviev, Dan Stefanescu, Lance Swenson, Gary Fireman Mathematics and Computer Science Department, Psychology Department Suffolk University, Boston June 22, 2012

What Is NSSI? Non-suicidal self-injury (NSSI) is the direct, deliberate destruction of one’s own body tissue in the absence of suicidal intent. It is practiced primarily by adolescents and young adults and is often concealed from others. Common NSSI activities include skin cutting, banging or hitting oneself, 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 have engaged in NSSI at some point in their lives NSSI is repeatedly found to be associated with significant emotional and behavioral dysfunction (e.g., eating disorders, suicide). Can we identify NSSI persons by automatically analyzing secondary data publicly available from massive online social networks (MOSN)? Dmitry Zinoviev, Dan Stefanescu, Lance Swenson, Gary Fireman Semantic Networks of Interests in Online NSSI Communities

NSSI and Massive Online Social Networks Many popular MOSNs (e.g., Facebook and LiveJournal) allow users to declare their interests. Associations between interest lists and NSSI community membership suggest that “likes” or interest lists may be serving as identity signals. Profile page with interests of a random NSSI LiveJournal user: Dmitry Zinoviev, Dan Stefanescu, Lance Swenson, Gary Fireman Semantic Networks of Interests in Online NSSI Communities

NSSI Communities in LiveJournal LiveJournal[.com] is a popular massive online blogging social network site (BSN)—a bimodal venue where users engage in both publishing and social activities. Bloggers can form contact lists, subscribe to their friends’ blogs, comment on selected blog posts, declare interests, and participate in communities—collective blogs. 32 mln individual and community accounts. 43 identified NSSI-related communities (some of them promote NSSI activities, while others advocate for NSSI abstinence) with 22,000 members and/or posters. The total number of harvested interests is ∼150,000, including misspelled, abbreviated, and hyphenated variants. Dmitry Zinoviev, Dan Stefanescu, Lance Swenson, Gary Fireman Semantic Networks of Interests in Online NSSI Communities

Users and Interests M is the adjacency matrix of a two-mode network of the NSSI users and their interests: Mij = 1 0 iff user Ui declares interest Vj else Should we use Pearson correlation (a.k.a. cosine distance) to calculate similarities of interests? Yes—if all users were different. However, a thematic community is likely to be more homogeneous than a random group of users. Dmitry Zinoviev, Dan Stefanescu, Lance Swenson, Gary Fireman Semantic Networks of Interests in Online NSSI Communities

Kova´z Correlations c B. Kova´z: “Two terms are similar with correlation Θij ∈ [−1, 1] if they c are used by similar people; two people are similar with correlation Φij ∈ [−1, 1] if they use similar terms.” (We calculate Φ but do not use it.) Initialize: Mi = Mi, − Mi, Mj = M ,j −M ,j Θij,0 = Φij,0 = δij Iterate: Θij,k+1 = Mi Φk MjT / Mi Φk MiT (M j Φk M jT ) Φij,k+1 = MiT Θk Mj / MiT Θk Mi (M jT Θk M j ) Dmitry Zinoviev, Dan Stefanescu, Lance Swenson, Gary Fireman Semantic Networks of Interests in Online NSSI Communities

From Θ to an Adjacency Matrix Θ is a dense symmetric signed square matrix with few or no zero terms. The distribution of Θij is close to uniform. Restricted to the top 600 most often declared interests (due to computational resources). Convert Θ to a sparse adjacency matrix Ψ: Ψij = Θij 0 Dmitry Zinoviev, Dan Stefanescu, Lance Swenson, Gary Fireman if Θij ≥ 0.8 else Semantic Networks of Interests in Online NSSI Communities

Semantic Map, as Seen by Gephi Dmitry Zinoviev, Dan Stefanescu, Lance Swenson, Gary Fireman Semantic Networks of Interests in Online NSSI Communities

Clustering Four major clusters: “music” (MUS), “pathology” (PAT), “daily life/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 and without the dash), spikes, weeds; DLE: cameras, cloths, dvds, flirting, flowers, fun, quotes, smiling, hearts (also as an HTML entity ♥ and as ♥); CRE: astrology, books, languages, philosophy, psychology, shakespeare, sociology, travel, wine. Dmitry Zinoviev, Dan Stefanescu, Lance Swenson, Gary Fireman Semantic Networks of Interests in Online NSSI Communities

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

Pathology Cluster The blowout of the PAT cluster: (A link between two interests means many people often mention these interests together.) Dmitry Zinoviev, Dan Stefanescu, Lance Swenson, Gary Fireman Semantic Networks of Interests in Online NSSI Communities

Are the Links NSSI Specific? Compare the NSSI communities to a random sample of LiveJournal users—doesn’t work, they share very few interests! Find appropriate communities that may share interests with the NSSI population: SMM “sexy-mood-music” (6,700 members, average age 25 years, music), M15M “movies-in-fifteen-minutes” (13,300 members, average age 28 years, video fans). Calculate the intersection between the NSSI semantic network and each of the other semantic networks under consideration. The intersection contains the associations that are significant for both communities and presumably are pathology-free. Dmitry Zinoviev, Dan Stefanescu, Lance Swenson, Gary Fireman Semantic Networks of Interests in Online NSSI Communities

Pathology-Free Networks Intersection of the NSSI and SMM networks: Well-defined CRE and DLE clusters. The MUS cluster is sparse and subdivided into movies and proper music. The PAT cluster is gone! Dmitry Zinoviev, Dan Stefanescu, Lance Swenson, Gary Fireman Semantic Networks of Interests in Online NSSI Communities

Leisure on Their Mind Our findings appear indicative of the growing global middle-class youth culture revolving around leisure activities (e.g., music, art) reflecting adolescent development in internationally-connected networks. This is supported by the similarities between the NSSI interested communities and the non-pathological comparison communities. Dmitry Zinoviev, Dan Stefanescu, Lance Swenson, Gary Fireman Semantic Networks of Interests in Online NSSI Communities

Conclusions We constructed a semantic network of interests declared by non-suicidal self-injury (NSSI) bloggers of LiveJournal. The network consists of four clearly separated interest clusters corresponding to the pathological terms, daily life, popular music, and creativity. The interests that bridge gaps between the pathology cluster and the other three clusters can be used as beacons signaling the potential presence of an NSSI behavior. The extent of MOSN NSSI-related communities on LiveJournal could evidence the limited opportunities for social networking among people. Dmitry Zinoviev, Dan Stefanescu, Lance Swenson, Gary Fireman Semantic Networks of Interests in Online NSSI Communities

Thanks! This research has been supported in part by the College of Arts and Sciences, Suffolk University, through an undergraduate research assistantship grant. The authors are grateful to Zo¨ Wells of Suffolk University for preliminary e data collection and Dr. Jim Hollander and Prof. John Boyd for suggestions on combining graphs. Dmitry Zinoviev, Dan Stefanescu, Lance Swenson, Gary Fireman Semantic Networks of Interests in Online NSSI Communities

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