A Novel Framework for Evaluating Polarization in Online Social Networks

This study aimed to investigate the role of neural adaptation in the perception of Haidinger's brushes, an entoptic phenomenon enabling the perception of polarization by the human visual system. The objective was to consider the rotational speed of the pattern in combination with the polarization level of the input light, to identify normal values in a cohort of healthy individuals. An experimental setup was designed and assembled using two LED sources, one of which was filtered by a rotating linear polarizer with adjustable speed. The relative intensity of the LEDs could be changed to tune the polarization level. A cohort of 37 healthy individuals underwent monocular testing at seven polarization levels. For each value, descending and ascending limit tests were conducted to determine the rotational speed thresholds. The rotational speed threshold decreases linearly with the logarithm of the polarization level. All participants could perceive the pattern down to at least 20% polarization, with a dramatic drop in the number of subjects at 10% and 5% polarization levels. The estimated average polarization threshold was 9.7 ± 1.2%. Lower initial rotational thresholds at maximum contrast corresponded with lower polarization thresholds. The study confirms the crucial role of rotational speed in the perception of Haidinger's brushes, especially at low polarization levels. As a normal pattern perception correlates with macular pigment distribution and density, those findings are essential for the development and calibration of screening devices based on Haidinger's brush perception for the early detection of maculopathy and other macular diseases.

Online social networks (OSNs) have become a commodity in our daily life. As an important concept in sociology and viral marketing, the study of social influence has received a lot of attentions in academia. Most of the existing proposals work well on dominant OSNs, such as Twitter, since these sites are mature and many users have generated a large amount of data for the calculation of social influence. Unfortunately, cold-start users on emerging OSNs generate much less activity data, which makes it challenging to identify potential influential users among them. In this work, we propose a practical solution to predict whether a cold-start user will become an influential user on an emerging OSN, by opportunistically leveraging the user’s information on dominant OSNs. A supervised machine learning-based approach is adopted, transferring the knowledge of both the descriptive information and dynamic activities on dominant OSNs. Descriptive features are extracted from the public data on a user’s homepage. In particular, to extract useful information from the fine-grained dynamic activities that cannot be represented by the statistical indices, we use deep learning technologies to deal with the sequential activity data. Using the real data of millions of users collected from Twitter (a dominant OSN) and Medium (an emerging OSN), we evaluate the performance of our proposed framework to predict prospective influential users. Our system achieves a high prediction performance based on different social influence definitions.

Deuterated 13C sites in sugars (D‐glucose and 2‐deoxy‐D‐glucose) showed 6.3‐to‐17.5‐fold higher solid‐state dynamic nuclear polarization (DNP) levels than their respective protonated sites at 3.35T. This effect was found to be unrelated to the protonation of the bath. Deuterated 15N in sites bound to exchangeable protons ([15N2]urea) showed a 1.3‐fold higher polarization than their respective protonated sites at the same magnetic field. This relatively smaller effect was attributed to incomplete deuteration of the 15N sites due to the solvent mixture. For a 15N site that is not bound to protons or deuterons ([15N]nitrate), deuteration of the bath did not affect the polarization level. These findings suggest a phenomenon related to DNP of X‐nuclei directly bound to deuteron(s) as opposed to proton(s). It appears that direct binding to deuterons increases the solid‐state DNP polarization level of X‐nuclei which are otherwise bound to protons.

Content Propagation in Online Social Networks

Ego network structure in online social networks and its impact on information diffusion

Nowadays, Online Social Networks (OSNs) has become one of the most common ways among people to facilitate communication. This has made it a target for attackers to steal information from influential users and has brought new forms of customized attacks for OSNs. Attackers take advantage of the user's trustworthiness when using OSN. This exploitation leads to attacks with a combination of both classical and modern threats. Specifically, colluding attackers have been taken advantage of many OSNs by creating fake profiles of friends of the target in the same OSN or others. Colluders impersonate their victims and ask friend requests to the target in the aim to infiltrate her private circle to steal information. These types of attacks are difficult to detect in OSNs because multiple malicious users may have a similar purpose to gain information from their targeted user. The purpose of this paper is to overcome this type of attack by addressing the problem of matching user profiles across multiple OSNs. Then, we will extract both features and text from a user's profile and build a classifier based on supervised learning techniques. Simulation and experimental results are provided to validate the accuracy of our findings.

The rapid development of online social networks (OSNs) renders them a powerful platform for information diffusion on a massive scale. OSNs generate enormous propagation traces. An important question is how to model the real-world information diffusion process. Although considerable studies have been conducted in this field, the temporal characteristics have not been fully addressed yet. This thesis addresses the issue of modeling the temporal dynamics of the information diffusion process. Based on empirical findings drawn from large-scale propagation traces of a popular OSN in China, we demonstrate that the temporal characteristics has a significant impact on the diffusion dynamics. Hence, a series of new temporal information diffusion models have been proposed by incorporating these temporal features. Experimental results demonstrate that these proposed models are more accurate and practical than existing discrete diffusion models. Moreover, one application of information diffusion models, i.e., the revenue maximization problem, is studied. Specifically, the thesis consists of three major parts: 1) preliminaries, i.e., introduction of research platform and collected dataset, 2) modeling social influence diffusion from three different temporal aspects, and 3) monetizing OSNs through designing intelligent pricing strategies in the diffusion process to realize the goal of revenue maximization.
\n
\nFirstly, the research platform is introduced and the statistical properties of the data derived from this platform are investigated. We choose Renren, the dominant social network website in China, as our research platform and study its information propagation mechanisms. Specifically, we concentrate on the propagation of “sharing video” behaviors, and collect data on more than 2.8 million Renren users and over 209 million diffusion traces. The analysis result shows that the video access patterns in OSNs differ significantly from Youtube-like systems, which makes understanding the video propagation behaviors in OSNs an important research task.
\n
\nSecondly, the temporal modeling of information diffusion is explored. By investigating temporal features using real diffusion traces, we find that three factors should be considered in building realistic diffusion models, including, information propagation latency, multiple influential sources and user diversities. We then develop models to explain the information propagation process by incorporating these factors, and demonstrate that the models reflect reality well.
\n
\nFinally, revenue maximization in the information diffusion process is studied. Specifically, the pricing factor is explicitly incorporated into the product diffusion process. To realize the goal of revenue maximization, we develop a Dynamic Programming Based Heuristic (DPBH) to obtain the optimal pricing sequence. Application of the DPBH in the revenue maximization problem shows that it performs well in both the expected revenue achieved and in running time. This leads to fundamental ramifications to many related OSN marketing applications.

As the online social network (OSN) develops, the social media data can be easily obtained, which leads to a large scale social network data. In our offline social networks, social relationships that an individual (ego) maintains with other people (alters) can also be organized into circles or groups according to the ego network model [1]. As to online social network, in order to help users cope with personal social network, many OSN sites provide services for users to group their friends. With many restrictions, such as on Facebook one cannot access other's personal information unless they establish ties with each other, the network structure is the only information known by users. In this paper, we propose an algorithm to find circles on personal social network mainly from the structural view. Based on the defined structural similarity and the condition where nodes constitute a circle, the algorithm is able to cluster the most similar nodes into the same circle. We then give proof to demonstrate the convergence of our algorithm. To show the effectiveness of our algorithm, we realize the algorithm on Facebook datasets. Compared with the community detection algorithm, the results are evaluated from the visualized quantified aspect. From the results, we find that community detection algorithm cannot directly deal with the circle detection problem due to the characteristic of ego network and our algorithm can effectively handle the circle detection on ego network.

Although migration has long been an imperative topic in social sciences, there are still needs of study on migrants’ unique and dynamic transnational identity, which heavily influences the social integration in the host society. In Online Social Network (OSN), where the contemporary migrants actively communicate and share their stories the most, different challenges against migrants’ belonging and identity and how they cope or reconcile may evidently exist. This paper aims to scrutinise how migrants are manifesting their belonging and identity via different technological types of online social networks, to understand the relations between online social networks and migrants’ multi-faceted transnational identity. The research introduces a comparative case study on an online social movement led by Koreans in Germany via their online communities, triggered by a German TV advertisement considered as stereotyping East Asians given by white supremacy’s point of view. Starting with virtual ethnography on three OSNs representing each of internet generations (Web 1.0 ~ Web 3.0), two-step Qualitative Data Analysis is carried out to examine how Korean migrants manifest their belonging and identity via their views on “who we are” and “who are others”. The analysis reveals how Korean migrants’ transnational identities differ by their expectation on the audience and the members in each online social network, which indicates that the distinctive features of the online platform may encourage or discourage them in shaping transnational identity as a group identity. The paper concludes with the two main emphases: first, current OSNs comprising different generational technologies play a significant role in understanding the migrants’ dynamic social values, and particularly, transnational identities. Second, the dynamics of migrants’ transnational identity engages diverse social and situational contexts. (keywords: transnational identity, migrants’ online social networks, stereotyping migrants, technological evolution of online social network).

Identification of influential users by neighbors in online social networks

Recent development of online social networks (OSNs) has accelerated communication between individuals and has led to efficient activities in the real world. However, activities on OSNs does not necessarily have positive impact on the real world. For example, explosive user dynamics such as flaming on OSNs seriously impacts not only on online communities but also on social activities in the real world. Therefore, to prevent the flaming, we need to understand the cause of explosive user dynamics. We have proposed to describe user dynamics in OSNs by the wave equation on networks which called the oscillation model of OSNs [1]. Although this model was motivated from a purely theoretical framework, it gives natural extension of the conventional node centrality measures [2], [3] and enables us to understand the cause of explosive user dynamics [1], [4]. However, its experimental verification is not sufficiently conducted. This paper discuss the validity of the oscillation model by analyzing real data.

With the rise and large-scale applications of social networking service, the prediction of information cascades has attracted extensive attention of researchers. User influence is an important factor affecting the dissemination of posts in online social networks. However, current studies usually take the number of users’ neighbors as their influence, and do not accurately describe the role of participating users in information dissemination. In this paper, a prediction model of information cascades in social networks is established based on the Hawkes process, and the model considers three factors, i.e., post influence, user influence and users’ response time, to describe the occurrence probability of forwarding events. In order to utilize abundant information of local network topology, we present a new method of calculating user influence, combining with semi-local centrality and local clustering coefficients. Then, a regression tree algorithm is used to determine time correction coefficients to reveal dynamic post influence, and the popularity prediction of posts in social networks is realized. Comparison experiments of different models are carried out on real-world datasets to evaluate the effectiveness and prediction performance of the proposed model, and results show that our method outperforms other counterparts.

Twitter has become one of largest social networks for users to broadcast burst topics. Influential users usually have a large number of followers and play an important role in the diffusion of burst topic. There have been many studies on how to detect influential users. However, traditional influential users detection approaches have largely ignored influential users in user community. In this paper, we investigate the problem of detecting community pacemakers. Community pacemakers are defined as the influential users that promote early diffusion in the user community of burst topic. To solve this problem, we present DCPBT, a framework that can detect community pacemakers in burst topics. In DCPBT, a burst topic user graph model is proposed, which can represent the topology structure of burst topic propagation across a large number of Twitter users. Based on the model, a user community detection algorithm based on random walk is applied to discover user community. For large-scale user community, we propose a ranking method to detect community pacemakers in each large-scale user community. To test our framework, we conduct the framework over Twitter burst topic detection system. Experimental results show that our method is more effective to detect the users that influence other users and promote early diffusion in the early stages of burst topic.

As per recent progress, online social network (OSN) users have grown tremendously worldwide, especially in the wake of the COVID-19 pandemic. Today, OSNs have become a core part of many people’s daily lifestyles. Therefore, increasing dependency on OSNs encourages privacy requirements to protect users from malicious sources. OSNs contain sensitive information about each end user that intruders may try to leak for commercial or non-commercial purposes. Therefore, ensuring different levels of privacy is a vital requirement for OSNs. Various privacy preservation methods have been introduced recently at the user and network levels, but ensuring k-anonymity and higher privacy model requirements such as l-diversity and t-closeness in OSNs is still a research challenge. This study proposes a novel method that effectively anonymizes OSNs using multiple-graph-properties-based clustering. The clustering method introduces the goal of achieving privacy of edge, node, and user attributes in the OSN graph. This clustering approach proposes to ensure k-anonymity, l-diversity, and t-closeness in each cluster of the proposed model. We first design the data normalization algorithm to preprocess and enhance the quality of raw OSN data. Then, we divide the OSN data into different clusters using multiple graph properties to satisfy the k-anonymization. Furthermore, the clusters ensure improved k-anonymization by a novel one-pass anonymization algorithm to address l-diversity and t-closeness privacy requirements. We evaluate the performance of the proposed method with state-of-the-art methods using a “Yelp real-world dataset”. The proposed method ensures high-level privacy preservation compared to state-of-the-art methods using privacy metrics such as anonymization degree, information loss, and execution time.

The outbreak of the COVID-19 pandemic triggered the perils of misinformation over social media. By amplifying the spreading speed and popularity of trustworthy information, influential social media users have been helping overcome the negative impacts of such flooding misinformation. In this article, we use the COVID-19 pandemic as a representative global health crisisand examine the impact of the COVID-19 pandemic on these influential users’ subjective well-being (SWB), one of the most important indicators of mental health. We leverage X (formerly known as Twitter) as a representative social media platform and conduct the analysis with our collection of 37,281,824 tweets spanning almost two years. To identify influential X users, we propose a new measurement called user bridging performance (UBM) to evaluate the speed and wideness gain of information transmission due to their sharing. With our tweet collection, we manage to reveal the more significant mental sufferings of influential users during the COVID-19 pandemic. According to this observation, through comprehensive hierarchical multiple regression analysis , we are the first to discover the strong relationship between individual social users’ subjective well-being and their bridging performance. We proceed to extend bridging performance from individuals to user subgroups. The new measurement allows us to conduct a subgroup analysis according to users’ multilingualism and confirm the bridging role of multilingual users in the COVID-19 information propagation. We also find that multilingual users not only suffer from a much lower SWB in the pandemic, but also experienced a more significant SWB drop.