Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userWho Will Share My Image? Predicting the Content Diffusion Path in Online Social Networks
76
0
0.0
(
0
)
Added by
Wenjian Hu
Publication date
2017
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Content popularity prediction has been extensively studied due to its importance and interest for both users and hosts of social media sites like Facebook, Instagram, Twitter, and Pinterest. However, existing work mainly focuses on modeling popularity using a single metric such as the total number of likes or shares. In this work, we propose Diffusion-LSTM, a memory-based deep recurrent network that learns to recursively predict the entire diffusion path of an image through a social network. By combining user social features and image features, and encoding the diffusion path taken thus far with an explicit memory cell, our model predicts the diffusion path of an image more accurately compared to alternate baselines that either encode only image or social features, or lack memory. By mapping individual users to user prototypes, our model can generalize to new users not seen during training. Finally, we demonstrate our models capability of generating diffusion trees, and show that the generated trees closely resemble ground-truth trees.
rate research
Read More
Online Social Networks (OSN) are among the most popular applications in todays Internet. Decentralized online social networks (DOSNs), a special class of OSNs, promise better privacy and autonomy than traditional centralized OSNs. However, ensuring availability of content when the content owner is not online remains a major challenge. In this paper, we rely on the structure of the social graphs underlying DOSN for replication. In particular, we propose that friends, who are anyhow interested in the content, are used to replicate the users content. We study the availability of such natural replication schemes via both theoretical analysis as well as simulations based on data from OSN users. We find that the availability of the content increases drastically when compared to the online time of the user, e. g., by a factor of more than 2 for 90% of the users. Thus, with these simple schemes we provide a baseline for any more complicated content replication scheme.
Image sharing on online social networks (OSNs) has become an indispensable part of daily social activities, but it has also led to an increased risk of privacy invasion. The recent image leaks from popular OSN services and the abuse of personal photos using advanced algorithms (e.g. DeepFake) have prompted the public to rethink individual privacy needs in OSN image sharing. However, OSN image privacy itself is quite complicated, and solutions currently in place for privacy management in reality are insufficient to provide personalized, accurate and flexible privacy protection. A more intelligent environment for privacy-friendly OSN image sharing is in demand. To fill the gap, we contribute a survey of privacy intelligence that targets modern privacy issues in dynamic OSN image sharing from a user-centric perspective. Specifically, we present a definition and a taxonomy of OSN image privacy, and a high-level privacy analysis framework based on the lifecycle of OSN image sharing. The framework consists of three stages with different principles of privacy by design. At each stage, we identify typical user behaviors in OSN image sharing and the privacy issues associated with these behaviors. Then a systematic review on the representative intelligent solutions targeting those privacy issues is conducted, also in a stage-based manner. The resulting analysis describes an intelligent privacy firewall for closed-loop privacy management. We also discuss the challenges and future directions in this area.
An increasing number of todays social interactions occurs using online social media as communication channels. Some online social networks have become extremely popular in the last decade. They differ among themselves in the character of the service they provide to online users. For instance, Facebook can be seen mainly as a platform for keeping in touch with close friends and relatives, Twitter is used to propagate and receive news, LinkedIn facilitates the maintenance of professional contacts, Flickr gathers amateurs and professionals of photography, etc. Albeit different, all these online platforms share an ingredient that pervades all their applications. There exists an underlying social network that allows their users to keep in touch with each other and helps to engage them in common activities or interactions leading to a better fulfillment of the services purposes. This is the reason why these platforms share a good number of functionalities, e.g., personal communication channels, broadcasted status updates, easy one-step information sharing, news feeds exposing broadcasted content, etc. As a result, online social networks are an interesting field to study an online social behavior that seems to be generic among the different online services. Since at the bottom of these services lays a network of declared relations and the basic interactions in these platforms tend to be pairwise, a natural methodology for studying these systems is provided by network science. In this chapter we describe some of the results of research studies on the structure, dynamics and social activity in online social networks. We present them in the interdisciplinary context of network science, sociological studies and computer science.
Social networks have become ubiquitous in our daily life, as such it has attracted great research interests recently. A key challenge is that it is of extremely large-scale with tremendous information flow, creating the phenomenon of Big Data. Under such a circumstance, understanding information diffusion over social networks has become an important research issue. Most of the existing works on information diffusion analysis are based on either network structure modeling or empirical approach with dataset mining. However, the information diffusion is also heavily influenced by network users decisions, actions and their socio-economic connections, which is generally ignored in existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic information diffusion process in social networks. Specifically, we analyze the framework in uniform degree and non-uniform degree networks and derive the closed-form expressions of the evolutionary stable network states. Moreover, the information diffusion over two special networks, ErdH{o}s-Renyi random network and the Barabasi-Albert scale-free network, are also highlighted. To verify our theoretical analysis, we conduct experiments by using both synthetic networks and real-world Facebook network, as well as real-world information spreading dataset of Twitter and Memetracker. Experiments shows that the proposed game theoretic framework is effective and practical in modeling the social network users information forwarding behaviors.
We present a method for accurately predicting the long time popularity of online content from early measurements of user access. Using two content sharing portals, Youtube and Digg, we show that by modeling the accrual of views and votes on content offered by these services we can predict the long-term dynamics of individual submissions from initial data. In the case of Digg, measuring access to given stories during the first two hours allows us to forecast their popularity 30 days ahead with remarkable accuracy, while downloads of Youtube videos need to be followed for 10 days to attain the same performance. The differing time scales of the predictions are shown to be due to differences in how content is consumed on the two portals: Digg stories quickly become outdated, while Youtube videos are still found long after they are initially submitted to the portal. We show that predictions are more accurate for submissions for which attention decays quickly, whereas predictions for evergreen content will be prone to larger errors.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
