Graph Neural Network for Customer Engagement Prediction on Social Media Platforms

Social media platforms such as Twitter and Facebook play a pivotal role in companies’ strategy of engaging customers. How to target potential customers on social media effectively and efficiently is an important yet unsolved question. Predicting customer engagement on social media platforms is facing several challenges that cannot be solved by traditional methods. In this work, we design a framework that leverages individual behavior on Facebook together with network contextual information to predict customer engagement (like/comment/share) of a brand’s posts. We first build a meta-path based Heterogeneous Information Network (HIN) to exploit large-scale content consumption information. We then design a Graph Neural Network (GNN) model combined with attention mechanism to learn structural feature representations of users to make the customer-brand engagement prediction. The proposed model is examined using a large-scale Facebook dataset and the result shows significant performance improvement compared with state-of-the-art baselines. Besides, the effectiveness of attention mechanism reveals the potential interpretability of the proposed model for the prediction results

Content Creator versus Brand Advertiser? The Effect of Inserting Advertisements in Videos on Influencers Engagement

Influencer advertising has become an indispensable component of online marketing due to the exponential growth of social influencers and their influence. Whereas the effectiveness of using influencer endorsements is well studied from the brand or company perspective, how the commercial endorsements affect influencers themselves is an important yet unrevealed question. We empirically examine the instantaneous (measured using live comment sentiment) and longer-term (measured using video feedback and follower number change) influence of inserting advertisements in videos on influencers’ reputation. We further investigate how this effect can be moderated when influencers demonstrate stronger endorsement by showing their faces during advertisements. Our result suggests that inserting advertisements have a negative impact on both instantaneous and longer-term viewer engagement; advertisements with influencers’ face showing moderate the negative effect of advertisements on viewers’ instantaneous response, while the different impact between advertisements with/out influencers showing their faces is not significant in the longer term

FGF18 Enhances Migration and the Epithelial-Mesenchymal Transition in Breast Cancer by Regulating Akt/GSK3β/Β-Catenin Signaling

Background/Aims: Fibroblast growth factors (FGFs) and their high-affinity receptors contribute to autocrine and paracrine growth stimulation in several human malignant tumors, including breast cancer. However, the mechanisms underlying the carcinogenic actions of FGF18 remain unclear. Methods: The transcription level of FGF18 under the hypoxic condition was detected with quantitative PCR (qPCR). A wound-healing assay was performed to assess the role of FGF18 in cell migration. A clonogenicity assay was used to determine whether FGF18 silencing affected cell clonogenicity. Western blotting was performed to investigate Akt/GSK3β/β-catenin pathway protein expression. Binding of β-catenin to the target gene promoter was determined by chromatin immunoprecipitation (ChIP) assays. Results: FGF18 promoted the epithelial-mesenchymal transition (EMT) and migration in breast cancer cells through activation of the Akt/GSK3β/β-catenin pathway. FGF18 increased Akt-Ser473 and -Thr308 phosphorylation, as well as that of GSK3β-Ser9. FGF18 also enhanced the transcription of proliferation-related genes (CDK2, CCND2, Ki67), metastasis-related genes (TGF-β, MMP-2, MMP-9), and EMT markers (Snail-1, Snail-2, N-cadherin, vimentin, TIMP1). β-catenin bound to the target gene promoter on the ChIP assay. Conclusion: FGF18 contributes to the migration and EMT of breast cancer cells following activation of the Akt/GSK3β/β-catenin pathway. FGF18 expression may be a potential prognostic therapeutic marker for breast cancer

Essay on Customer Engagement Analytics in Social Media Using Deep Learning

Brands are increasingly using social media platforms such as Facebook and Twitter to promote their services and products and engage with their customers. With the increasing prevalence of various social media platforms, intensive online marketing competition has emerged among companies. How to target potential customers in social media effectively and efficiently is an important yet unsolved problem. In the meantime, some newly emerged formats of advertisement have become indispensable components of online marketing, such as influencer marketing where brands collaborate with social media influencers to broadcast products or services. While the usefulness of leveraging influencer endorsements from the standpoint of a brand or corporation has been well examined, a crucial but unanswered topic is how commercial endorsements affect the influencers themselves. In this dissertation, I explore the algorithms and strategies that benefit brands and content providers in the competitive environment on social media. Specifically, from an algorithmic perspective, I develop novel Artificial Intelligence frameworks with interpretability grounded in behavioral and social theories to target potential customers on social media, which in turn prompt brand profitability and market performance. In addition, I combine econometrics with deep learning, text mining, and computer vision techniques, to understand the mechanism of online viewer behavior for influencer marketing. This study contributes to our understanding of how to engage customers for online marketing. The outcome provides stakeholders with a strong forecast and a comprehensive understanding of customer engagement mechanisms. This dissertation also brings practical information in the competitive climate of online marketing

Random vibration response prediction of electronic device based on hierarchicaly model updating and validation

Electronic device is susceptible to failure due to environmental vibration during transportation and use, even causing the entire device to failure. In recent years, the structure of electronic device has been more complex, the application range has become more and more extensive, and requirements for structural performance under vibration environment of electronic device are more strict, especially in the aviation and aerospace field. Therefore, in order to ensure the safe and reliable work of electronic device, and to control vibration level effectively , it is necessary to carry out environmental vibration simulation analysis and response prediction for electronic device.In this paper, a certain type of aviation airborne electronic device is taken as the research object. The finite element method is used to model the electronic device. In order to obtain an accurate finite element model to simulate the vibration of the electronic device, the hierarchicaly model updating and validation is applied to the vibration simulation for the electronic device. Then the substructure FE model (PCB printed circuit board without electronic items) is firstly calibrated by means of the deterministic model updating technique by comparing with the experiment modal analysis. And a Bayesian method of parameter uncertainty quantification was employed to identify the mass and stiffness distribution (PCB with electronic items). Finally, the established validated model is used to predict the random response of the reference points and verified by random vibration test.<br/

Research on evaluation index method of cloud-network convergence capability

Abstract There is no measurable and evaluable index system for cloud-network convergence that provides guidance and reference for the subsequent construction and development of cloud-network convergence. It is a big project to select and evaluate the indexes of cloud-network convergence, which requires suitable index selection and index evaluation schemes. Based on analytic hierarchy process (AHP) and entropy weight method, this paper proposes an improved AHP (i-AHP) index selection scheme and index evaluation scheme leveraging the years of experts’ experience, the geometric mean and the least square method. The improved weighted least square method (WLSM) is finally proved to be more stable for index evaluation scheme by adding abnormal data. In addition, the index weight obtained by the index evaluation scheme with WLSM are provided as a reference for the future development of cloud-network convergence. The simulation results show that the proposed scheme is superior to the existing index evaluation scheme and can avoid the weight deviation caused by the disturbance and fluctuation of abnormal data

Raid the Chat Room: The Effects of Group Size on User Engagement in Online Synchronized Communication

Online synchronous platforms, such as live streaming, spend tremendous efforts engaging users in a real-time setting, which has gained considerable popularity very recently. While the existing literature finds that the group size of peers positively affects user engagement on asynchronous platforms, the effect of group size remains unexplored in the context of synchronous streaming. In this work, we leverage the unique raid functionality, an exogenous increase in live streaming viewers, and empirically examine how group size affects users’ real-time commenting engagement. Collecting and analyzing chat history in 13,382 playbacks on Twitch, our result suggests that existing viewers (users who engage in the live streaming channel before the raid) tend to engage less after the raid. The findings in this paper indicate a negative effect of group size on viewer engagement in the synchronous communication setting, which theoretically extends the prior literature in user engagement and crowd effects

Slicing Resource Allocation for eMBB and URLLC in 5G RAN

This paper investigates the network slicing in the virtualized wireless network. We consider a downlink orthogonal frequency division multiple access system in which physical resources of base stations are virtualized and divided into enhanced mobile broadband (eMBB) and ultrareliable low latency communication (URLLC) slices. We take the network slicing technology to solve the problems of network spectral efficiency and URLLC reliability. A mixed-integer programming problem is formulated by maximizing the spectral efficiency of the system in the constraint of users’ requirements for two slices, i.e., the requirement of the eMBB slice and the requirement of the URLLC slice with a high probability for each user. By transforming and relaxing integer variables, the original problem is approximated to a convex optimization problem. Then, we combine the objective function and the constraint conditions through dual variables to form an augmented Lagrangian function, and the optimal solution of this function is the upper bound of the original problem. In addition, we propose a resource allocation algorithm that allocates the network slicing by applying the Powell–Hestenes–Rockafellar method and the branch and bound method, obtaining the optimal solution. The simulation results show that the proposed resource allocation algorithm can significantly improve the spectral efficiency of the system and URLLC reliability, compared with the adaptive particle swarm optimization (APSO), the equal power allocation (EPA), and the equal subcarrier allocation (ESA) algorithm. Furthermore, we analyze the spectral efficiency of the proposed algorithm with the users’ requirements change of two slices and get better spectral efficiency performance

Effects of Exogenous Melatonin on Methyl Viologen-Mediated Oxidative Stress in Apple Leaf

Oxidative stress is a major source of damage of plants exposed to adverse environments. We examined the effect of exogenous melatonin (MT) in limiting of oxidative stress caused by methyl viologen (MV; paraquatin) in apple leaves (Malus domestica Borkh.). When detached leaves were pre-treated with melatonin, their level of stress tolerance increased. Under MV treatment, melatonin effectively alleviated the decrease in chlorophyll concentrations and maximum potential Photosystem II efficiency while also mitigating membrane damage and lipid peroxidation when compared with control leaves that were sprayed only with water prior to the stress experiment. The melatonin-treated leaves also showed higher activities and transcripts of antioxidant enzymes superoxide dismutase, peroxidase, and catalase. In addition, the expression of genes for those enzymes was upregulated. Melatonin-synthesis genes MdTDC1, MdT5H4, MdAANAT2, and MdASMT1 were also upregulated under oxidative stress in leaves but that expression was suppressed in response to 1 mM melatonin pretreatment during the MV treatments. Therefore, we conclude that exogenous melatonin mitigates the detrimental effects of oxidative stress, perhaps by slowing the decline in chlorophyll concentrations, moderating membrane damage and lipid peroxidation, increasing the activities of antioxidant enzymes, and changing the expression of genes for melatonin synthesis

Ectopic expression of AANAT or HIOMT improves melatonin production and enhances UV-B tolerance in transgenic apple plants

Melatonin is involved in plant responses to various environmental stresses. Although many studies have demonstrated that the tolerance of plants to stress is improved by exogenous melatonin, the role of endogenous melatonin metabolism in the response of apples to UV-B stress remains unclear. Here, the human melatonin biosynthesis-related enzyme genes AANAT or HIOMT were transformed into 'GL-3' apple, and the transgenic lines were treated with UV-B stress. The ectopic expression of AANAT or HIOMT significantly increased the melatonin content in apples. After UV-B stress, the tolerance of apple lines with ectopic expression of AANAT or HIOMT was markedly improved. The decrease in chlorophyll fluorescence, the generation of reactive oxygen species and the shrinkage of stomata caused by UV-B stress were alleviated by AANAT or HIOMT ectopic expression. In addition, the total phenolic content was markedly increased in the transgenic lines compared with the WT (wild type). The increase in phenolic compounds was related to the increase in benzoic acid, hydroxycinnamic acid, dihydrochalcones and flavanols, among which increases in chlorogenic acid, phloridzin and procyanidin B1 content were most prominent. Furthermore, the transgenic lines did not only promote the expression of genes related to phenolic synthesis under UV-B stress, but they also increased the accumulation of phenolic compounds by inhibiting the expression of MdPPO and MdPOD related to phenolic degradation. In summary, our results demonstrate that AANAT- or HIOMT-mediated melatonin synthesis improved the tolerance of apples to UV-B stress, mainly by scavenging reactive oxygen species, increasing photosynthetic capacity and increasing total phenolic content