Content In Online Social Networks Research Articles

Viral marketing branching processes

We consider the inherent timeline structure of the appearance of content in online social networks (OSNs) while studying content propagation. We model the propagation of a post/content of interest by an appropriate multi-type branching process. The branching process allows one to predict the emergence of global macro properties (e.g., the spread of a post in the network) from the laws and parameters that determine local interactions. The local interactions largely depend upon the timeline (an inverse stack capable of holding many posts and one dedicated to each user) structure and the number of friends (i.e., connections) of users, etc. We explore the use of multi-type branching processes to analyze the viral properties of the post, e.g., to derive the expected number of shares, the probability of virality of the content, etc.In OSNs, the new posts push down the existing contents in timelines, which can greatly influence content propagation; our analysis considers this influence. We find that one leads to draw incorrect conclusions when the timeline (TL) structure is ignored: (a) for instance, even less attractive posts are shown to get viral; (b) ignoring TL structure also indicates erroneous growth rates. More importantly, one cannot capture some interesting paradigm shifts/phase transitions; for example, virality chances are not monotone with network activity parameter, as shown by analysis including TL influence.In the last part, we integrate the online auctions into our viral marketing model. We study the optimization problem considering real-time bidding. We again compared the study with and without considering the TL structure for varying activity levels of the network. We find that the analysis without TL structure fails to capture the relevant phase transitions, thereby making the study incomplete.

Incremental Group-Level Popularity Prediction in Online Social Networks

Predicting the popularity of web contents in online social networks is essential for many applications. However, existing works are usually under non-incremental settings. In other words, they have to rebuild models from scratch when new data occurs, which are inefficient in big data environments. It leads to an urgent need for incremental prediction, which can update previous results with new data and conduct prediction incrementally. Moreover, the promising direction of group-level popularity prediction has not been well treated, which explores fine-grained information while keeping a low cost. To this end, we identify the problem of incremental group-level popularity prediction, and propose a novel model IGPP to address it. We first predict the group-level popularity incrementally by exploiting the incremental CANDECOMP/PARAFCAC (CP) tensor decomposition algorithm. Then, to reduce the cumulative error by incremental prediction, we propose three strategies to restart the CP decomposition. To the best of our knowledge, this is the first work that identifies and solves the problem of incremental group-level popularity prediction. Extensive experimental results show significant improvements of the IGPP method over other works both in the prediction accuracy and the efficiency.

PANOLA: A Personal Assistant for Supporting Users in Preserving Privacy

Privacy is the right of individuals to keep personal information to themselves. When individuals use online systems, they should be given the right to decide what information they would like to share and what to keep private. When a piece of information pertains only to a single individual, preserving privacy is possible by providing the right access options to the user. However, when a piece of information pertains to multiple individuals, such as a picture of a group of friends or a collaboratively edited document, deciding how to share this information and with whom is challenging. The problem becomes more difficult when the individuals who are affected by the information have different, possibly conflicting privacy constraints. Resolving this problem requires a mechanism that takes into account the relevant individuals’ concerns to decide on the privacy configuration of information. Because these decisions need to be made frequently (i.e., per each piece of shared content), the mechanism should be automated. This article presents a personal assistant to help end-users with managing the privacy of their content. When some content that belongs to multiple users is about to be shared, the personal assistants of the users employ an auction-based privacy mechanism to regulate the privacy of the content. To do so, each personal assistant learns the preferences of its user over time and produces bids accordingly. Our proposed personal assistant is capable of assisting users with different personas and thus ensures that people benefit from it as they need it. Our evaluations over multiagent simulations with online social network content show that our proposed personal assistant enables privacy-respecting content sharing.

TPS: A Topological Potential Scheme to Predict Influential Network Nodes for Intelligent Communication in Social Networks

The growing popularity of Online Social Networks (OSN) have prompted an increasing number of companies to promote their brands and products through social media. This paper presents a topological potential scheme for predicting influential nodes from large scale OSNs to support more intelligent brand communication. We first construct a weighted network model for the users and their relationships extracted from the brand-related content in OSNs. We quantitatively measure the individual value of the nodes from both the network structure and brand engagement aspects. Moreover, we have addressed the problem of influence decay along with information propagation in social networks and use the topological potential theory to evaluate the importance of the nodes by their individual values as well as the individual values of their surrounding nodes. The experimental results have shown that the proposed method is able to predict influential nodes in large-scale OSNs. We investigate the top-k influential nodes identified by our method in detail, which are quite different from those identified by using pure network structure or individual value. We can obtain an identification result with a higher ratio of verified users and user coverage by using our method compared to existing typical approaches.

Towards better social crisis data with HERMES: Hybrid sensing for EmeRgency ManagEment System

People involved in mass emergencies increasingly publish information-rich contents in Online Social Networks (OSNs), thus acting as a distributed and resilient network of human sensors. In this work we present HERMES, a system designed to enrich the information spontaneously disclosed by OSN users in the aftermath of disasters. HERMES leverages a mixed data collection strategy, called hybrid sensing, and state-of-the-art AI techniques. Evaluated in real-world emergencies, HERMES proved to increase: (i) the amount of the available damage information; (ii) the density (up to 7×) and the variety (up to 18×) of the retrieved geographic information; (iii) the geographic coverage (up to 30%) and granularity.

A model for the influence of media on the ideology of content in online social networks

Many people rely on online social networks as sources of news and information, and the spread of media content with ideologies across the political spectrum influences online discussions and impacts actions offline. To examine the impact of media in online social networks, we generalize bounded-confidence models of opinion dynamics by incorporating media accounts as influencers in a network. We quantify partisanship of content with a continuous parameter on an interval, and we formulate higher-dimensional generalizations to incorporate content quality and increasingly nuanced political positions. We simulate our model with one and two ideological dimensions, and we use the results of our simulations to quantify the "entrainment" of content from non-media accounts to the ideologies of media accounts in a network. We maximize media impact in a social network by tuning the number of media accounts that promote the content and the number of followers of the accounts. Using numerical computations, we find that the entrainment of the ideology of content spread by non-media accounts to media ideology depends on a network's structural features, including its size, the mean number of followers of its nodes, and the receptiveness of its nodes to different opinions. We then introduce content quality --- a key novel contribution of our work --- into our model. We incorporate multiple media sources with ideological biases and quality-level estimates that we draw from real media sources and demonstrate that our model can produce distinct communities ("echo chambers") that are polarized in both ideology and quality. Our model provides a step toward understanding content quality and ideology in spreading dynamics, with ramifications for how to mitigate the spread of undesired content and promote the spread of desired content.

Advanced Machine Learning Technique to Handle Filtering Unwanted Messages in Online Social Networks

Social networks are increased in present days because of communication between different users like Face book, Google and Twitter. Major fundamental issue behind online social networks is control user’s messages in-front of sharing rumor related messages and posts unwanted messages. It is still main challenge to define user’s share other user’s details in social network communication. In this paper, Greedy Heuristic based Advanced Short Text Classifier (GHASTC) used to classify filtering with rumor related classification for multi user’s interaction in social networks. This hybrid approach gives direct control to users to control unwanted data posted on own space. The proposed approach works with rule based filtering system, which consists a customized filtering for unwanted content in online social networks. The experimental results show efficient filtering results with comparison of traditional techniques.

Exploring demographic information in online social networks for improving content classification

The daily interaction between users within online social networks (OSNs) is an effective way to analyze and interpret its context in real time in order to capture the interests, preferences, and concerns of the OSNs users. These offer a unique information source for several applications in several fields such as trendsetting, future prediction, recommendation systems, community detection, and marketing. Most of the existing studies on text classification in OSNs rely on content based approach, in order to capture users interests through exploiting and categorizing the unstructured textual content shared by those users according to their topics. Moreover, users public profiles available on OSNs often reveal their demographic attributes such as age, gender, education, marital status, etc., which can play an essential role in identifying users interests and preferences. User demographic attributes can provide some preferences for some topics of interests. People with different demographic attributes may be interested in different topics, while people with similar demographic attributes may have the same interests. Usually, young people are more interested in technology than old people, who are more interested in the political news than young people. In this paper, we propose a demographic-content-based approach which uses both users demographic attributes and the textual content to classify OSNs posts using six classifiers ANN, k-NN, Naïve Bayes, Decision Tree, Decision rules and SVM. The experiments are done on a large Facebook dataset in order to analyze the effect of these demographic attributes on the performance of the categorization of the shared textual content in OSNs.

The unconscious side of Facebook: Do online social network profiles leak cues to users’ implicit motive dispositions?

In this study we investigated the links between motive dispositions and online social network (OSN) profile content. We assessed the achievement, affiliation and power motives via self- and peer-report. In addition, we used a projective test and two novel, affect based measures (involving affect ratings and EMG recordings) to assess implicit motives in the three content domains. Two observers independently coded motive-specific OSN content. Results showed that self-reports were linked to OSN content for the power domain. Peer-reports and measures of implicit motives were positively linked to OSN content across motive domains. In most cases, measures of implicit motives were still linked to OSN content after adjusting for self- and peer-reports. These results indicate that OSN profiles may leak cues to users’ implicit motives, which neither users themselves nor their peers are aware of. Implications for motive theory, motive assessment, and targeted online advertising will be discussed.

CBL: Exploiting Community Based Locality for Efficient Content Search Service in Online Social Networks

Retrieving relevant data for users in online social network (OSN) systems is a challenging problem. Cassandra, a storage system used by popular OSN systems, such as Facebook and Twitter, relies on a DHT-based scheme to randomly partition the personal data of users among servers across multiple data centers. Although DHT is highly scalable for hosting a large number of users (personal data), it leads to costly inter-server communications across data centers due to the complex interconnection and interaction among OSN users. In this paper, we explore how to retrieve the OSN content in a cost-effective way by retaining the simple and robust nature of OSNs. Our approach exploits a simple, yet powerful principle called Community-Based Locality (CBL), which posits that if a user has a one-hop neighbor within a particular community, it is very likely that the user has other one-hop neighbors inside the same community. We demonstrate the existence of community-based locality in diverse traces of popular OSN systems such as Facebook, Orkut, Flickr, Youtube, and Livejournal. Based on the observation, we design a CBL-based algorithm to build the content index in OSN systems. By partitioning and indexing the relevant data of users within a community on the same server in the data center, the CBL-based index avoids a significant amount of inter-server communications during searching, making retrieving relevant data for a user in large-scale OSNs efficient. In addition, by using CBL-based scheme we can provide much faster search response and balanced loads. We conduct comprehensive trace-driven simulations to evaluate the performance of the proposed scheme. Results show that ourscheme significantly reduces the network traffic by 73 percent while reduces the query latency by 35 percent compared with existing schemes.

A Descriptive Study of the Prevalence and Typology of Alcohol-Related Posts in an Online Social Network for Smoking Cessation.

Alcohol use and problem drinking are associated with smoking relapse and poor smoking-cessation success. User-generated content in online social networks for smoking cessation provides an opportunity to understand the challenges and treatment needs of smokers. This study used machine-learning text classification to identify the prevalence, sentiment, and social network correlates of alcohol-related content in the social network of a large online smoking-cessation program, BecomeAnEX.org. Data were analyzed from 814,258 posts (January 2012 to May 2015). Posts containing alcohol keywords were coded via supervised machine-learning text classification for information about the user's personal experience with drinking, whether the user self-identified as a problem drinker or indicated problem drinking, and negative sentiment about drinking in the context of a quit attempt (i.e., alcohol should be avoided during a quit attempt). Less than 1% of posts were related to alcohol, contributed by 13% of users. Roughly a third of alcohol posts described a personal experience with drinking; very few (3%) indicated "problem drinking." The majority (70%) of alcohol posts did not express negative sentiment about drinking alcohol during a quit attempt. Users who did express negative sentiment about drinking were more centrally located within the network compared with those who did not. Discussion of alcohol was rare, and most posts did not signal the need to quit or abstain from drinking during a quit attempt. Featuring expert information or highlighting discussions that are consistent with treatment guidelines may be important steps to ensure smokers are educated about drinking risks.

Image steganography using uncorrelated color space and its application for security of visual contents in online social networks

Image steganography is a growing research field, where sensitive contents are embedded in images, keeping their visual quality intact. Researchers have used correlated color space such as RGB, where modification to one channel affects the overall quality of stego-images, hence decreasing its suitability for steganographic algorithms. Therefore, in this paper, we propose an adaptive LSB substitution method using uncorrelated color space, increasing the property of imperceptibility while minimizing the chances of detection by the human vision system. In the proposed scheme, the input image is passed through an image scrambler, resulting in an encrypted image, preserving the privacy of image contents, and then converted to HSV color space for further processing. The secret contents are encrypted using an iterative magic matrix encryption algorithm (IMMEA) for better security, producing the cipher contents. An adaptive LSB substitution method is then used to embed the encrypted data inside the V-plane of HSV color model based on secret key-directed block magic LSB mechanism. The idea of utilizing HSV color space for data hiding is inspired from its properties including de-correlation, cost-effectiveness in processing, better stego image quality, and suitability for steganography as verified by our experiments, compared to other color spaces such as RGB, YCbCr, HSI, and Lab. The quantitative and qualitative experimental results of the proposed framework and its application for addressing the security and privacy of visual contents in online social networks (OSNs), confirm its effectiveness in contrast to state-of-the-art methods.

Crossing borders with social media: Online social networks and FDI

Social media is embedded in today's internationalization strategy. Companies extend their reach into foreign countries by posting and tweeting. Firms also enhance their mobile capabilities in foreign markets (e.g., knowledge and reputation) through user-generated content in online social networks. Levering on the capabilities-based theory of the multinational enterprise, this paper builds upon a resource-based, industry/network-based, and institution-based view framework. The study provides a comprehensive conceptual and empirical model to explain the effect of social networks on foreign direct investment. Empirical analysis in a global panel dataset of >4500 multinational enterprises suggests that online social networks' activity stimulates foreign capital expenditure and new affiliates. In addition, the article explores the relevance of customer capabilities along with sectoral and institutional moderating effects.

The impact of negative online social network content on expressed sentiment, executive function, and working memory

Given the paucity of research examining emotional states in social networking, the aim of this study was to examine the impact of negatively valanced social media network content on the amount of sentiment that participants included in their free-text responses, as well as on their executive functioning and working memory. Eighty participants aged between 18 and 67 (M = 29.39, SD = 11.21 years) completed baseline mood and cognitive measures (working memory and executive functioning) before exposure to three control posts and one negative emotional post. For each post, participants wrote a free-text response or indicated that they would not respond. Participants then completed the mood and cognitive measures a second time. After exposure to an emotionally negative post, participants' mood was lower and their executive functioning improved (as measured by reaction time and number of incorrectly identified target words). Participants' responses to an emotional post contained higher levels of sentiment compared to their responses to control posts. After controlling for demographic variables, participants' mood and trait empathy predicted the level of sentiment that they included in their responses to the emotional post. Mood, executive function, and trait empathy contributed to individuals’ online social network engagement for emotionally negative posts.

The Challenge of Improving Credibility of User-Generated Content in Online Social Networks

In every environment of information exchange, Information Quality (IQ) is considered one of the most important issues. Studies in Online Social Networks (OSNs) analyze a number of related subjects that span both theoretical and practical aspects, from data quality identification and simple attribute classification to quality assessment models for various social environments. Among several factors that affect information quality in online social networks is the credibility of user-generated content. To address this challenge, some proposed solutions include community-based evaluation and labeling of user-generated content in terms of accuracy, clarity, and timeliness, along with well-established real-time data mining techniques.

Differentially Private Online Learning for Cloud-Based Video Recommendation With Multimedia Big Data in Social Networks

With the rapid growth in multimedia services and the enormous offers of video contents in online social networks, users have difficulty in obtaining their interests. Therefore, various personalized recommendation systems have been proposed. However, they ignore that the accelerated proliferation of social media data has led to the big data era, which has greatly impeded the process of video recommendation. In addition, none of them has considered both the privacy of users' contexts (e,g., social status, ages and hobbies) and video service vendors' repositories, which are extremely sensitive and of significant commercial value. To handle the problems, we propose a cloud-assisted differentially private video recommendation system based on distributed online learning. In our framework, service vendors are modeled as distributed cooperative learners, recommending videos according to user's context, while simultaneously adapting the video-selection strategy based on user-click feedback to maximize total user clicks (reward). Considering the sparsity and heterogeneity of big social media data, we also propose a novel geometric differentially private model, which can greatly reduce the performance (recommendation accuracy) loss. Our simulation shows the proposed algorithms outperform other existing methods and keep a delicate balance between computing accuracy and privacy preserving level.

Mining Influencers Using Information Flows in Social Streams

The problem of discovering information flow trends in social networks has become increasingly relevant due to the increasing amount of content in online social networks, and its relevance as a tool for research into the content trends analysis in the network. An important part of this analysis is to determine the key patterns of flow in the underlying network. Almost all the work in this area has focused on fixed models of the network structure, and edge-based transmission between nodes. In this article, we propose a fully content-centered model of flow analysis in networks, in which the analysis is based on actual content transmissions in the underlying social stream, rather than a static model of transmission on the edges. First, we introduce the problem of influence analysis in the context of information flow in networks. We then propose a novel algorithm InFlowMine to discover the information flow patterns in the network and demonstrate the effectiveness of the discovered information flows using an influence mining application. This application illustrates the flexibility and effectiveness of our information flow model to find topic- or network-specific influencers, or their combinations. We empirically show that our information flow mining approach is effective and efficient than the existing methods on a number of different measures.

Recognition on Online Social Network by user's writing style

Compromising legitimate accounts is the most popular way of disseminating fraudulent content in Online Social Networks (OSN). To address this issue, we propose an approach for recognition of compromised Twitter accounts based on Authorship Verification. Our solution can detect accounts that became compromised by analysing their user writing styles. This way, when an account content does not match its user writing style, we affirm that the account has been compromised, similar to Authorship Verification. Our approach follows the profile-based paradigm and uses N-grams as its kernel. Then, a threshold is found to represent the boundary of an account writing style. Experiments were performed using two subsampled datasets from Twitter. Experimental results showed the developed model is very suitable for compromised recognition of Online Social Networks accounts due to the capacity of recognizing user styles over 95% accuracy for both datasets.

Online reputation measurement of companies based on user-generated content in online social networks

Social media websites such as Facebook, Twitter, etc. has changed the way peoples communicate and make decision. In this regard, various companies are willing to use these media to raise their reputation. In this paper, a reputation management system is proposed which measures the reputation of a given company by using the social media data, particularly tweets of Twitter. Taking into account the name of the company and its' related tweets, it is determined that a given tweet has either negative or positive impact on the company's reputation or product. The proposed method is based on N-gram learning approach, which consists of two steps: train step and test step. In the training step, we consider four profiles i.e. positive, negative, neutral, and irrelevant profiles for each company. Then 80% of the available tweets are used to build the companies' profiles. Each profile contains the terms that have been appeared in the tweets of each company together with the terms' frequencies. Then in the test step, which is performed on the 20% remaining tweets of the dataset, each tweet is compared with all of the built profiles, based on distance criterion to examine how the given tweet affects a company's reputation. Evaluation of the proposed method indicates that this method has a better efficiency and performance in terms of recall and precision compared to the previous methods such as Neural Network and Bayesian method.

Privacy and Security for Multimedia Content shared on OSNs: Issues and Countermeasures

A key aspect of online social networks (OSNs) is the user-generated multimedia content shared online. OSNs like Facebook have to deal with up to 300 million photos uploaded on a daily basis, both video- and audio-related social networks have also started to gain important shares of the market. Although the security and privacy mechanisms deployed by OSNs can cope with several risks and discourage inexperienced users from malicious behaviours, many issues still need to be addressed. Uploaded multimedia content carries information that could be transmitted virally and almost instantaneously within OSNs and beyond. OSNs could be seen as a multimedia heaven for users. However, in many cases they might end up being the user's personal hell with information disclosure or distortion, contrary to his/her will. In this article, we outline the most significant security and privacy issues related to the exposure of multimedia content in OSNs and we discuss possible countermeasures.