Importance of Social Network Structures in Influencer Marketing

As collaborations between brands and influencers become increasingly popular, predicting the capacity of an influencer to generate engagement has garnered increasing attention from researchers. Traditionally, managers have been relying on follower-based statistics to identify individuals with potential to reach a vast number of users on social-media. However, this approach may often direct managers to accounts with millions of followers accompanied with high recruiting costs. In this paper, we argue that the network structure of influencers is a useful measure for capturing an influencer’s ability to generate engagement. Using Instagram data, we perform a deep-learning analysis on the social network of influencers and show that the network structure explains a large share of the variations in user engagement, even outperforming traditionally used variables such as the number of followers in the case of comments. This study contributes to the emergent literature on the importance of social ties in the digital environmen

FairGRAPE: Fairness-aware GRAdient Pruning mEthod for Face Attribute Classification

Existing pruning techniques preserve deep neural networks' overall ability to make correct predictions but may also amplify hidden biases during the compression process. We propose a novel pruning method, Fairness-aware GRAdient Pruning mEthod (FairGRAPE), that minimizes the disproportionate impacts of pruning on different sub-groups. Our method calculates the per-group importance of each model weight and selects a subset of weights that maintain the relative between-group total importance in pruning. The proposed method then prunes network edges with small importance values and repeats the procedure by updating importance values. We demonstrate the effectiveness of our method on four different datasets, FairFace, UTKFace, CelebA, and ImageNet, for the tasks of face attribute classification where our method reduces the disparity in performance degradation by up to 90% compared to the state-of-the-art pruning algorithms. Our method is substantially more effective in a setting with a high pruning rate (99%). The code and dataset used in the experiments are available at https://github.com/Bernardo1998/FairGRAPEComment: To appear in ECCV 202

InfluencerRank: Discovering Effective Influencers via Graph Convolutional Attentive Recurrent Neural Networks

As influencers play considerable roles in social media marketing, companies increase the budget for influencer marketing. Hiring effective influencers is crucial in social influencer marketing, but it is challenging to find the right influencers among hundreds of millions of social media users. In this paper, we propose InfluencerRank that ranks influencers by their effectiveness based on their posting behaviors and social relations over time. To represent the posting behaviors and social relations, the graph convolutional neural networks are applied to model influencers with heterogeneous networks during different historical periods. By learning the network structure with the embedded node features, InfluencerRank can derive informative representations for influencers at each period. An attentive recurrent neural network finally distinguishes highly effective influencers from other influencers by capturing the knowledge of the dynamics of influencer representations over time. Extensive experiments have been conducted on an Instagram dataset that consists of 18,397 influencers with their 2,952,075 posts published within 12 months. The experimental results demonstrate that InfluencerRank outperforms existing baseline methods. An in-depth analysis further reveals that all of our proposed features and model components are beneficial to discover effective influencers.Comment: ICWSM 202

Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning

Bipolar disorder (BD) is closely associated with an increased risk of suicide. However, while the prior work has revealed valuable insight into understanding the behavior of BD patients on social media, little attention has been paid to developing a model that can predict the future suicidality of a BD patient. Therefore, this study proposes a multi-task learning model for predicting the future suicidality of BD patients by jointly learning current symptoms. We build a novel BD dataset clinically validated by psychiatrists, including 14 years of posts on bipolar-related subreddits written by 818 BD patients, along with the annotations of future suicidality and BD symptoms. We also suggest a temporal symptom-aware attention mechanism to determine which symptoms are the most influential for predicting future suicidality over time through a sequence of BD posts. Our experiments demonstrate that the proposed model outperforms the state-of-the-art models in both BD symptom identification and future suicidality prediction tasks. In addition, the proposed temporal symptom-aware attention provides interpretable attention weights, helping clinicians to apprehend BD patients more comprehensively and to provide timely intervention by tracking mental state progression.Comment: KDD 2023 accepte

Modeling and Discovering Authentic and Effective Influencers on Social Media via Graph Neural Network Learning

Influencer marketing, a word-of-mouth marketing strategy that leverages prominent individuals on social media, has been gaining great traction in recent years. As the number of influencers has explosively increased with the rapid growth of the influencer marketing industry, several issues in evaluating and discovering valuable influencers have been raised, including the influencer fraud problem. These issues arise from utilizing solely quantitative metrics such as the number of followers to identify influencers on social media. Although these quantitative indicators can measure the influence of social media users to some extent, it is challenging to evaluate the quality of influencers with such metrics.In this dissertation, we propose several graph learning frameworks that incorporate various input sources from social media including text, images, and graphs, to evaluate influencers in the first place. To that end, we build and analyze social networks of influencers based on the posting, interacting, and advertising behaviors of influencers to find valuable insights by understanding the social relations of influencers. The frameworks presented in this dissertation take the influencer social networks with decent features from multi-modal inputs and then assess qualities of influencers, including transparency, loyalty, authenticity, and efficiency of influencers. As a result, the proposed methodologies not only address pressing issues in the influencer marketing industry but also advance multi-modal graph learning-based applications

D-vlog: Multimodal Vlog Dataset for Depression Detection

Detecting depression based on non-verbal behaviors has received great attention. However, most prior work on detecting depression mainly focused on detecting depressed individuals in laboratory settings, which are difficult to be generalized in practice. In addition, little attention has been paid to analyzing the non-verbal behaviors of depressed individuals in the wild. Therefore, in this paper, we present a multimodal depression dataset, D-Vlog, which consists of 961 vlogs (i.e., around 160 hours) collected from YouTube, which can be utilized in developing depression detection models based on the non-verbal behavior of individuals in real-world scenario. We develop a multimodal deep learning model that uses acoustic and visual features extracted from collected data to detect depression. Our proposed model employs the cross-attention mechanism to effectively capture the relationship across acoustic and visual features, and generates useful multimodal representations for depression detection. The extensive experimental results demonstrate that the proposed model significantly outperforms other baseline models. We believe our dataset and the proposed model are useful for analyzing and detecting depressed individuals based on non-verbal behavior

InfluencerRank: Discovering Effective Influencers via Graph Convolutional Attentive Recurrent Neural Networks

As influencers play considerable roles in social media marketing, companies increase the budget for influencer marketing. Hiring effective influencers is crucial in social influencer marketing, but it is challenging to find the right influencers among hundreds of millions of social media users. In this paper, we propose InfluencerRank that ranks influencers by their effectiveness based on their posting behaviors and social relations over time. To represent the posting behaviors and social relations, the graph convolutional neural networks are applied to model influencers with heterogeneous networks during different historical periods. By learning the network structure with the embedded node features, InfluencerRank can derive informative representations for influencers at each period. An attentive recurrent neural network finally distinguishes highly effective influencers from other influencers by capturing the knowledge of the dynamics of influencer representations over time. Extensive experiments have been conducted on an Instagram dataset that consists of 18,397 influencers with their 2,952,075 posts published within 12 months. The experimental results demonstrate that InfluencerRank outperforms existing baseline methods. An in-depth analysis further reveals that all of our proposed features and model components are beneficial to discover effective influencers

Measurement and Analysis of BitTorrent Traffic in Mobile WiMAX Networks

Abstract—As mobile Internet environments are becoming dom-inant, how to revamp P2P operations for mobile hosts is gaining more and more attention. In this paper, we carry out empirical traffic measurement of BitTorrent service in various settings (static, bus and subway) in commercial WiMAX networks. To this end, we analyze the connectivity among peers, the down-load throughput/stability, and the signaling overhead of mobile WiMAX hosts in comparison to a wired (Ethernet) host. We find out the drawbacks of BitTorrent operations in mobile Internet are characterized by lower connection ratio, unstable connections amongst peers, and higher control message overhead. I

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This study aimed to establish an optimal extraction process and high-performance liquid chromatography (HPLC)-photodiode array (PDA) analytical method for determination of marker compounds, dihydrokaempferol (DHK) and 3-O-methylquercetin (3-MeQ), as a part of materials standardization for the development of health functional foods from stems of Opuntia ficus-indica var. saboten (OFS). The quantitative determination method of marker compounds was optimized by HPLC analysis, and the correlation coefficient for the calibration curve showed very good linearity. The HPLC-PDA method was applied successfully to quantification of marker compounds in OFS after validation of the method in terms of linearity, accuracy, and precision. Ethanolic extracts from stems of O. ficus-indica var. saboten (OFSEs) were evaluated by reflux extraction at 70 and 80??C with 50, 70, and 80% ethanol for 3, 4, 5, and 6 h. Among OFSEs, OFS70E at 80??C showed the highest contents of DHK and 3-MeQ of 26.42??0.65 and 3.88??0.29 mg/OFS 100 g, respectively. Furthermore, OFSEs were determined for their antioxidant activities by measuring 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging and lipid peroxidation (LPO) inhibitory activities in rat liver homogenate. OFS70E at 70??C showed the most potent antioxidant activities with IC50 values of 1.19??0.11 and 0.89??0.09 mg/mL in the DPPH radical scavenging and LPO inhibitory assays, respectively. To identify active components of OFS, various chromatographic separation of OFS70E led to isolation of 11 flavonoids: dihydrokaempferol, dihydroquercetin, 3-O-methylquercetin, quercetin, isorhamnetin 3-O-glucoside, isorhamnetin 3-O-galactoside, narcissin, kaempferol 7-O-glucoside, quercetin 3-O-galactoside, isorhamnetin, and kaempferol 3-O-rutinoside. The results suggest that standardization of DHK in OFSEs using HPLC-PDA analysis would be an acceptable method for the development of health functional foods.clos