Advisor: Kun-Ta Chuang Presenter: Wei Lee g Online Social Data for Detecting Social Network Mental Disorders Hong-Han Shuai1, Chih-Ya Shen1, De-Nian Yang1, Yi-Feng Lan2, Wang-Chien Lee3, Philip S. Yu4,5, Ming-Syan Chen6 1Academia Sinica, Taiwan 2Tamkang University, Taiwan 3The Pennsylvania State University, USA 4University of Illinois at Chicago, USA 5Tsinghua University, China 6National Taiwan University, Taiwan [email protected], [email protected], [email protected] carolyﬂ[email protected], [email protected], [email protected], [email protected] mber of social network mental disorders (SN- yber-Relationship Addiction, Information Over- professor of the Social Studies of Science and Technology in MIT.1 With the explosive growth in popularity of social net- working and messaging apps, online social networks (OSNs) have become a part of many people’s daily lives. While OSNs
ratio between number of actions from you and number of actions to you • Action • e.g. Likes, Comments, Posts • Loneliness is one of the primary reason of SNMD
| • The ration between online actions and ofﬂine actions • Ofﬂine Actions • e.g. Checking logs, “Going” Events • SNMD people tends to snub their friends in real life
feed from friends' walls • SS creates more pleasure than SB • SS is more likely to be a drug reward (Possible SNMD) • SB: Passively reading personal news feeds
N Users • M OSN sources • Extract a latent feature matrix U • Used To estimate deﬁcit features • From Corresponding features of other OSNs • From Other users with similar behavior element (i, j, k) of a three-mode tensor T is denoted by tijk , whereas the i-th row and the j-th column of a two-dimensional matrix U are respectively denoted by ui: and u:j . Speciﬁcally, Tucker decomposition [31] of a tensor T ∈ RN×D×M is deﬁned as: T = C ×1 U ×2 V ×3 W, (1) where U ∈ RN×R, V ∈ RD×S and W ∈ RM×T are latent matrices. In this paper, the matrix of users’ latent features U plays a crucial role. In Tucker decomposition, R, S, and T are parameters to be set according to diﬀerent criteria [31]. The 1-mode product of C ∈ RR×S×T and U ∈ RN×R, denoted by C ×1 U, is a ma- trix with size N × S × T, where each element (C ×1 U) nst = R r=1 crst urn . Given the input tensor matrix T that consists of the features of all users from every OSN, Tucker decompo- sition derives C, U, V, and W to meet the above equality on while vectors are denoted by boldface lowercase letters, e.g., u. Matrices are represented by boldface capital letters, e.g., U, and tensors are denoted by calligraphic letters, e.g., T . Each element (i, j, k) of a three-mode tensor T is denoted by tijk , whereas the i-th row and the j-th column of a two-dimensional matrix U are respectively denoted by ui: and u:j . Speciﬁcally, Tucker decomposition [31] of a tensor T ∈ RN×D×M is deﬁned as: T = C ×1 U ×2 V ×3 W, (1) where U ∈ RN×R, V ∈ RD×S and W ∈ RM×T are latent matrices. In this paper, the matrix of users’ latent features U plays a crucial role. In Tucker decomposition, R, S, and T are parameters to be set according to diﬀerent criteria [31]. The 1-mode product of C ∈ RR×S×T and U ∈ RN×R, denoted by C ×1 U, is a ma- trix with size N × S × T , where each element (C ×1 U) nst = R r=1 crst urn . Given the input tensor matrix T that consists of the features of all users from every OSN, Tucker decompo- sition derives C, U, V, and W to meet the above equality on Tndm for every n, d, and m, where C needs to be diagonal, and U, V , and W are required to be orthogonal [31]. By regard- ing ui: in U as the latent features of user i, we can eﬃciently integrate the information from diﬀerent networks for i. whereas the i-th row and the j-th column of a two-dimensional matrix U are respectively denoted by ui: and u:j . Speciﬁcally, Tucker decomposition [31] of a tensor T ∈ RN×D×M is deﬁned as: T = C ×1 U ×2 V ×3 W, (1) where U ∈ RN×R, V ∈ RD×S and W ∈ RM×T are latent matrices. In this paper, the matrix of users’ latent features U plays a crucial role. In Tucker decomposition, R, S, and T are parameters to be set according to diﬀerent criteria [31]. The 1-mode product of C ∈ RR×S×T and U ∈ RN×R, denoted by C ×1 U, is a ma- trix with size N × S × T , where each element (C ×1 U) nst = R r=1 crst urn . Given the input tensor matrix T that consists of the features of all users from every OSN, Tucker decompo- sition derives C, U, V, and W to meet the above equality on Tndm for every n, d, and m, where C needs to be diagonal, and U, V , and W are required to be orthogonal [31]. By regard- ing ui: in U as the latent features of user i, we can eﬃciently integrate the information from diﬀerent networks for i. Equipped with tensor decomposition on T , we propose a new SNMD-based Tensor Model (STM) to minimize the fol- lowing objective function L, L(U, V, W, C) = 1 2 ∥T − C ×1 U ×2 V ×3 W∥2 λ1 T λ2 2 Notice that factors for a does not en friends to b increases in pearing in t be smaller i Therefore, t acteristics o i.e., the user auxiliary in To prope weighted ad the Laplaci where D is We present each elemen gradient, w follows: ∇ui: L = ∇ vi: L =
V, W need to be orthogonal element (i, j, k) of a three-mode tensor T is denoted by tijk , whereas the i-th row and the j-th column of a two-dimensional matrix U are respectively denoted by ui: and u:j . Speciﬁcally, Tucker decomposition [31] of a tensor T ∈ RN×D×M is deﬁned as: T = C ×1 U ×2 V ×3 W, (1) where U ∈ RN×R, V ∈ RD×S and W ∈ RM×T are latent matrices. In this paper, the matrix of users’ latent features U plays a crucial role. In Tucker decomposition, R, S, and T are parameters to be set according to diﬀerent criteria [31]. The 1-mode product of C ∈ RR×S×T and U ∈ RN×R, denoted by C ×1 U, is a ma- trix with size N × S × T, where each element (C ×1 U) nst = R r=1 crst urn . Given the input tensor matrix T that consists of the features of all users from every OSN, Tucker decompo- sition derives C, U, V, and W to meet the above equality on while vectors are denoted by boldface lowercase letters, e.g., u. Matrices are represented by boldface capital letters, e.g., U, and tensors are denoted by calligraphic letters, e.g., T . Each element (i, j, k) of a three-mode tensor T is denoted by tijk , whereas the i-th row and the j-th column of a two-dimensional matrix U are respectively denoted by ui: and u:j . Speciﬁcally, Tucker decomposition [31] of a tensor T ∈ RN×D×M is deﬁned as: T = C ×1 U ×2 V ×3 W, (1) where U ∈ RN×R, V ∈ RD×S and W ∈ RM×T are latent matrices. In this paper, the matrix of users’ latent features U plays a crucial role. In Tucker decomposition, R, S, and T are parameters to be set according to diﬀerent criteria [31]. The 1-mode product of C ∈ RR×S×T and U ∈ RN×R, denoted by C ×1 U, is a ma- trix with size N × S × T , where each element (C ×1 U) nst = R r=1 crst urn . Given the input tensor matrix T that consists of the features of all users from every OSN, Tucker decompo- sition derives C, U, V, and W to meet the above equality on Tndm for every n, d, and m, where C needs to be diagonal, and U, V , and W are required to be orthogonal [31]. By regard- ing ui: in U as the latent features of user i, we can eﬃciently integrate the information from diﬀerent networks for i. whereas the i-th row and the j-th column of a two-dimensional matrix U are respectively denoted by ui: and u:j . Speciﬁcally, Tucker decomposition [31] of a tensor T ∈ RN×D×M is deﬁned as: T = C ×1 U ×2 V ×3 W, (1) where U ∈ RN×R, V ∈ RD×S and W ∈ RM×T are latent matrices. In this paper, the matrix of users’ latent features U plays a crucial role. In Tucker decomposition, R, S, and T are parameters to be set according to diﬀerent criteria [31]. The 1-mode product of C ∈ RR×S×T and U ∈ RN×R, denoted by C ×1 U, is a ma- trix with size N × S × T , where each element (C ×1 U) nst = R r=1 crst urn . Given the input tensor matrix T that consists of the features of all users from every OSN, Tucker decompo- sition derives C, U, V, and W to meet the above equality on Tndm for every n, d, and m, where C needs to be diagonal, and U, V , and W are required to be orthogonal [31]. By regard- ing ui: in U as the latent features of user i, we can eﬃciently integrate the information from diﬀerent networks for i. Equipped with tensor decomposition on T , we propose a new SNMD-based Tensor Model (STM) to minimize the fol- lowing objective function L, L(U, V, W, C) = 1 2 ∥T − C ×1 U ×2 V ×3 W∥2 λ1 T λ2 2 Notice that factors for a does not en friends to b increases in pearing in t be smaller i Therefore, t acteristics o i.e., the user auxiliary in To prope weighted ad the Laplaci where D is We present each elemen gradient, w follows: ∇ui: L = ∇ vi: L =
the above equality on Tndm for every n, d, and m, where C needs to be diagonal, and U, V , and W are required to be orthogonal [31]. By regard- ing ui: in U as the latent features of user i, we can eﬃciently integrate the information from diﬀerent networks for i. Equipped with tensor decomposition on T , we propose a new SNMD-based Tensor Model (STM) to minimize the fol- lowing objective function L, L(U, V, W, C) = 1 2 ∥T − C ×1 U ×2 V ×3 W∥2 + λ1 2 tr(UT La U) + λ2 2 ∥U∥2, (2) where tr(·) denotes the matrix traces, the Frobenius norm of a tensor T is deﬁned as ∥T ∥ = √ < T , T >, and λ1 and λ2 are parameters controlling the contribution of each part during the above collaborative factorization. L ﬁrst minimizes the decomposition error, i.e., ∥T −C×1 U×2 V×3 W∥2, for T . Note that Eq. (1) does not always need to hold since other crucial goals are also incorporated in the model. For example, the term that minimizes ∥U∥2 is to derive a more concise latent th w W e g fo A fe th fa le
the above equality on Tndm for every n, d, and m, where C needs to be diagonal, and U, V , and W are required to be orthogonal [31]. By regard- ing ui: in U as the latent features of user i, we can eﬃciently integrate the information from diﬀerent networks for i. Equipped with tensor decomposition on T , we propose a new SNMD-based Tensor Model (STM) to minimize the fol- lowing objective function L, L(U, V, W, C) = 1 2 ∥T − C ×1 U ×2 V ×3 W∥2 + λ1 2 tr(UT La U) + λ2 2 ∥U∥2, (2) where tr(·) denotes the matrix traces, the Frobenius norm of a tensor T is deﬁned as ∥T ∥ = √ < T , T >, and λ1 and λ2 are parameters controlling the contribution of each part during the above collaborative factorization. L ﬁrst minimizes the decomposition error, i.e., ∥T −C×1 U×2 V×3 W∥2, for T . Note that Eq. (1) does not always need to hold since other crucial goals are also incorporated in the model. For example, the term that minimizes ∥U∥2 is to derive a more concise latent th w W e g fo A fe th fa le Decomposition Error
the above equality on Tndm for every n, d, and m, where C needs to be diagonal, and U, V , and W are required to be orthogonal [31]. By regard- ing ui: in U as the latent features of user i, we can eﬃciently integrate the information from diﬀerent networks for i. Equipped with tensor decomposition on T , we propose a new SNMD-based Tensor Model (STM) to minimize the fol- lowing objective function L, L(U, V, W, C) = 1 2 ∥T − C ×1 U ×2 V ×3 W∥2 + λ1 2 tr(UT La U) + λ2 2 ∥U∥2, (2) where tr(·) denotes the matrix traces, the Frobenius norm of a tensor T is deﬁned as ∥T ∥ = √ < T , T >, and λ1 and λ2 are parameters controlling the contribution of each part during the above collaborative factorization. L ﬁrst minimizes the decomposition error, i.e., ∥T −C×1 U×2 V×3 W∥2, for T . Note that Eq. (1) does not always need to hold since other crucial goals are also incorporated in the model. For example, the term that minimizes ∥U∥2 is to derive a more concise latent th w W e g fo A fe th fa le Regularization
the above equality on Tndm for every n, d, and m, where C needs to be diagonal, and U, V , and W are required to be orthogonal [31]. By regard- ing ui: in U as the latent features of user i, we can eﬃciently integrate the information from diﬀerent networks for i. Equipped with tensor decomposition on T , we propose a new SNMD-based Tensor Model (STM) to minimize the fol- lowing objective function L, L(U, V, W, C) = 1 2 ∥T − C ×1 U ×2 V ×3 W∥2 + λ1 2 tr(UT La U) + λ2 2 ∥U∥2, (2) where tr(·) denotes the matrix traces, the Frobenius norm of a tensor T is deﬁned as ∥T ∥ = √ < T , T >, and λ1 and λ2 are parameters controlling the contribution of each part during the above collaborative factorization. L ﬁrst minimizes the decomposition error, i.e., ∥T −C×1 U×2 V×3 W∥2, for T . Note that Eq. (1) does not always need to hold since other crucial goals are also incorporated in the model. For example, the term that minimizes ∥U∥2 is to derive a more concise latent th w W e g fo A fe th fa le
the above equality on Tndm for every n, d, and m, where C needs to be diagonal, and U, V , and W are required to be orthogonal [31]. By regard- ing ui: in U as the latent features of user i, we can eﬃciently integrate the information from diﬀerent networks for i. Equipped with tensor decomposition on T , we propose a new SNMD-based Tensor Model (STM) to minimize the fol- lowing objective function L, L(U, V, W, C) = 1 2 ∥T − C ×1 U ×2 V ×3 W∥2 + λ1 2 tr(UT La U) + λ2 2 ∥U∥2, (2) where tr(·) denotes the matrix traces, the Frobenius norm of a tensor T is deﬁned as ∥T ∥ = √ < T , T >, and λ1 and λ2 are parameters controlling the contribution of each part during the above collaborative factorization. L ﬁrst minimizes the decomposition error, i.e., ∥T −C×1 U×2 V×3 W∥2, for T . Note that Eq. (1) does not always need to hold since other crucial goals are also incorporated in the model. For example, the term that minimizes ∥U∥2 is to derive a more concise latent th w W e g fo A fe th fa le • Take the output U as the feature vectors of users
Parasociality Game posts Median of BI Median of BI Online/oﬄine ratio Online/oﬄine ratio Sticker number Parasociality SD of BL Online/oﬄine ratio Number of selﬁes Sticker number CC. CC. Parasociality Acc.: 80.2% Acc.: 76.8% Acc.: 82.7% Table 5: Feature eﬀectiveness analysis: SNMDD ac- curacy on the FB US dataset. Used Features Accuracy Used Features Accuracy PR 56.9% All–PR 78.2% ONOFF 60.3% All–ONOFF 75.1% SC 40.1% All–SC 78.8% SSB 44.4% All–SSB 79.3% SD 58.9% All–SD 73.2% TEMP 67.5% All–TEMP 68.1% UT 36.4% All–UT 82.6% DIS 54.0% All–DIS 75.9% PROF 18.2% All–PROF 81.5% All 83.1% personal features, e.g., the temporal behavior features, can be 5 3 6 7 2 1 9 4 8
Not useful for IO, CR • Since NC are less socially active • Parasociality • Effective on all SNMD types (Especially for CR) • Burst intensity and length • Useful for detecting IO • PROF is the least important
Nu Number of Features (a) Relative improvement w.r.t. the number of fea- tures. 0 2 4 6 8 10 12 Relat in Nu Number of Features (b) Relative improvement w.r.t. the number of latent features (STM ). Figure 2: Relative accuracy change with respect to number of features. 0% 15% 30% 45% 60% 75% CR NC IO NA Propotion of Friends with SNMDs in FB_L SNMD Type CR NC IO NA (a) SNMD types of friends (FB L). 0% 15% 30% 45% 60% CR NC IO NA Propotion of Friends with SNMDs in IG_L SNMD Type CR NC IO NA (b) SNMD types of friends (IG L). 20% 40% 60% MD Users in ty of FB_L CR NC IO 40% 60% 80% MD Users in ty of IG_L CR NC IO se in us A th SN co clo IG ﬁr m N te co sm les ar Th st us