Differential Use,Differential Effects: Investigating the Roles of Different Modes of News Use in Promoting Political Participation

The present study clarifies the role of the informational use of media in facilitating citizens' political participation by focusing on three distinctive modes of news experience (i.e., news reading, posting, and endorsing) within the context of online social networking sites (SNSs). The findings show that news posting contributes to greater participation in politics by positively mediating the relationship between the use of SNSs and participatory activities. In contrast, news reading and endorsing were not significantly associated with political participation. In addition, the study found that the indirect effects of SNS use on participation via news posting are highest among those with a high degree of network political expertise and lower among those with an average level of such expertise.     

How do people compare themselves with others on social network sites?: The case of Facebook

The social comparison theory and its subsequent studies say that comparing with others can influence an individual in several ways (e.g., evaluation of oneself, influence on self-esteem/self-confidence, and efficient decision making) and people compare with others when they are confronted with information of others. With the popularity of social network sites, many people acquire or are exposed to information of others on social network sites, which implies that people are likely to frequently engage in social comparison behavior on social network sites. The present paper examines social comparison behavior on social network sites (especially on Facebook) using a college students sample. We find that an individual’s personality characteristics (i.e., social comparison orientation, self-esteem, self-uncertainty, and self-consciousness) influence the person’s social comparison frequency on Facebook. A positive relationship between Facebook use intensity and social comparison frequency on Facebook is found. In addition, we find a positive association between social comparison frequency on Facebook and the frequency of having a negative feeling from comparison. Other findings are also reported in the paper.     

Queer identity online: Informal learning and teaching experiences of LGBTQ individuals on social media

In this study, we explored how social media, particularly social networking sites, serve as informal learning environments for lesbian, gay, bisexual, transgender, questioning, and otherwise-identified (LGBTQ) individuals during formative stages of their evolving LGBTQ identity. We conducted semi-structured interviews (N = 33) probing LGBTQ individuals’ use of social media and identified three educational uses tied to online information seeking: traditional learning (e.g., information seeking about LGBTQ-related issues), social learning (e.g., observing role models or other LGBTQ individuals’ behavior and experiences), and experiential learning (e.g., experimenting with online dating sites and dating apps). These experiences were especially common during the coming out process. Participants also reported a fourth educational function, teaching (e.g., sharing information with others about their experiences as an LGBTQ individual). Teaching was more common among individuals who were out and those with less common identities (e.g., asexual and transgender). Several affordances of social media, including visibility, association, persistence, anonymity, and interactivity enabled these learning experiences.     

GameSense: game-like in-image advertising

Considering the continuously increasing availability and accessibility of multimedia contents via social networking sites, our research addresses how to monetize the social multimedia contents with an efficient advertising approach. This paper presents a novel game-like advertising system called GameSense, which is driven by the compelling contents of online images. The contextually relevant ads (i.e., product logos) are embedded at appropriate positions within the online games, which are created on the basis of online images. The ads are selected based on multimodal relevance, i.e. text relevance, user relevance and visual content similarity. The game is able to provide viewers rich experience and thus promotes the embedded ads to provide more effective advertising. GameSense represents one of the first attempts toward effective online mashup applications which connect a photo-sharing site with an advertising agency. The effectiveness of GameSense is evaluated over a large-scale real world image set.     

Context-aware trust network extraction in large-scale trust-oriented social networks

In recent years, social networking sites have been used as a means for a rich variety of activities, such as movie recommendations and product recommendations. In order to evaluate the trust between a truster (i.e., the source) and a trustee (i.e., the target) who have no direct interaction in Online Social Networks (OSNs), the trust network between them that contains important intermediate participants, the trust relations between the participants, and the social context, has an important influence on trust evaluation. Thus, to deliver a reasonable trust evaluation result, before performing any trust evaluation (i.e., trust transitivity), the contextual trust network from a given source to a given target needs to be first extracted from the social network, where constraints on social context should also be considered to guarantee the quality of the extracted networks. However, this problem has been proved to be NP-Complete. Towards solving this challenging problem, we first present a contextual trust-oriented social network structure which takes social contextual impact factors, including trust, social intimacy degree, community impact factor, preference similarity and residential location distance into account. These factors have significant influences on both social interactions between participants and trust evaluation. Then, we present a new concept QoTN (Quality of Trust Network) and propose a social context-aware trust network extraction model. Finally, we propose a Heuristic Social Context-Aware trust Network extraction algorithm (H-SCAN-K) by extending the K-Best-First Search (KBFS) method with several proposed optimization strategies. The experiments conducted on two real datasets illustrate that our proposed model and algorithm outperform the existing methods in both algorithm efficiency and the quality of the extracted trust networks.     

Discovery of unusual regional social activities using geo-tagged microblogs

The advent of microblogging services represented by Twitter evidently stirred a popular trend of personal update sharing from all over the world. Furthermore, the recent mobile device and wireless network technologies are greatly expanding the connectivity between people over the social networking sites. Regarding the shared buzzes over the sites as a crowd-sourced database reflecting a various kind of real-world events, we are able to conduct a variety of social analytics using the crowd power in much easier ways. In this paper, we propose a geo-social event detection method by finding out unusually crowded places based on the conception of social networking sites as a social event detector. In order to detect unusual statuses of a region, we previously construct geographical regularities deduced from geo-tagged microblogs. Especially, we utilize a large number of geo-tagged Twitter messages which are collected by means of our own tweets acquisition method in terms of geographic relevancy. By comparing to those regularities, we decide if there are any unusual events happening in monitoring geographical areas. Finally, we describe the experimental results to evaluate the proposed unusuality detection method on the basis of geographical regularities which are computed from a large number of real geo-tagged tweet dataset around Japan.     

Can learning be virtually boosted? An investigation of online social networking impacts

Online social networking has deeply penetrated university campuses, influencing multiple aspects of student life. We investigate the impacts of individual online social networking engagement (e.g., on Facebook) from a pedagogical standpoint. Based on social learning theory, we argue that two socialization processes, social acceptance and acculturation, bridge individual online social networking engagement with three domains of social learning outcomes. Results from a survey accompanied by focus group discussions demonstrate the substantial impacts of university student online social networking engagement on social learning processes and outcomes. Online social networking not only directly influences university students’ learning outcomes, but also helps the students attain social acceptance from others and adapt to university culture, both of which play prominent roles in improving their learning outcomes.     

Can learning be virtually boosted? An investigation of online social networking impacts

Online social networking has deeply penetrated university campuses, influencing multiple aspects of student life. We investigate the impacts of individual online social networking engagement (e.g., on Facebook) from a pedagogical standpoint. Based on social learning theory, we argue that two socialization processes, social acceptance and acculturation, bridge individual online social networking engagement with three domains of social learning outcomes. Results from a survey accompanied by focus group discussions demonstrate the substantial impacts of university student online social networking engagement on social learning processes and outcomes. Online social networking not only directly influences university students’ learning outcomes, but also helps the students attain social acceptance from others and adapt to university culture, both of which play prominent roles in improving their learning outcomes.     

Insights from consumer interactions on a social networking site: Findings from six apparel retail brands

Consumer social interactions on a social networking site have been discussed in the literature as information sources on consumer negative voice, product market research, and trend forecasting. The present study contributes to this line of research by identifying insights that a retail brand can derive from consumer social interactions about a competing retail brand. Based on 2,573,620 consumer social interactions related to six apparel retailers on the social networking site Facebook, this study analyzes the fan number, brand posting and response behavior, and consumer activities. The number of fans correlates with retail sales and the number of stores. However, the fan number is not an indicator of (social) displeasure. Consumer activities such as commenting, liking, and sharing, provide significant indications of these situations. As posting and response behavior reflect central elements of brand strategies on social networking sites, this study proposes a framework for identifying these strategies and the positioning of competing brands. For the six apparel retail brands, we identified a prototypical representative for three of four social interaction strategies.     

Minimizing information disclosure to third parties in social login platforms

Over the past few years, a large and ever increasing number of Web sites have incorporated one or more social login platforms and have encouraged users to log in with their Facebook, Twitter, Google, or other social networking identities. Research results suggest that more than two million Web sites have already adopted Facebook’s social login platform, and the number is increasing sharply. Although one might theoretically refrain from such social login features and cross-site interactions, usage statistics show that more than 250 million people might not fully realize the privacy implications of opting-in. To make matters worse, certain Web sites do not offer even the minimum of their functionality unless users meet their demands for information and social interaction. At the same time, in a large number of cases, it is unclear why these sites require all that personal information for their purposes. In this paper, we mitigate this problem by designing and developing a framework for minimum information disclosure in social login interactions with third-party sites. Our example case is Facebook, which combines a very popular single sign-on platform with information-rich social networking profiles. Whenever users want to browse to a Web site that requires authentication or social interaction using a Facebook identity, our system employs, by default, a Facebook session that reveals the minimum amount of information necessary. Users have the option to explicitly elevate that Facebook session in a manner that reveals more or all of the information tied to their social identity. This enables users to disclose the minimum possible amount of personal information during their browsing experience on third-party Web sites.     

Calculus interdependency,personality contingency,and causal asymmetry: Toward a configurational privacy calculus model of information disclosure

This study proposes a configurational privacy calculus model (CPCM) to understand calculus interdependency, personality contingency, and causal asymmetry in the information disclosure behavior in social networking sites (SNS). The results based on qualitative comparative analysis (QCA) showed that high disclosure can be caused by the configurations with the presence of at least one perceived benefit among the three types of benefits (i.e., self-expression, life documentation, and social rewards) while the absence of privacy risks, or the configurations with the presence of both perceived benefits and privacy risks meanwhile with the presence of behavioral activation system (BAS) but the absence of behavioral inhibition system (BIS). In contrast, low disclosure can be caused by the configurations with the absence of all the three types of perceived benefits, or the configurations with the presence of both perceived benefits and privacy risks meanwhile with the presence of BIS, but the absence of BAS.     

Document-level sentiment classification using hybrid machine learning approach

It is a practice that users or customers intend to share their comments or reviews about any product in different social networking sites. An analyst usually processes to reviews properly to obtain any meaningful information from it. Classification of sentiments associated with reviews is one of these processing steps. The reviews framed are often made in text format. While processing the text reviews, each word of the review is considered as a feature. Thus, selection of right kind of features needs to be carried out to select the best feature from the set of all features. In this paper, the machine learning algorithm, i.e., support vector machine, is used to select the best features from the training data. These features are then given input to artificial neural network method, to process further. Different performance evaluation parameters such as precision, recall, f-measure, accuracy have been considered to evaluate the performance of the proposed approach on two different datasets, i.e., IMDb dataset and polarity dataset.     

The effect of gratifications derived from use of the social networking site Qzone on Chinese adolescents’ positive mood

Drawing from Uses and Gratifications Theory, this study explores the influence of the gratifications derived from use of the social networking site Qzone on Chinese adolescents’ positive mood. Qzone is the social networking site that is most preferred and used by Chinese adolescents. Hypothesized relationships are analyzed by structural equation analysis in a sample of 220 Chinese adolescents aged 14–19 with an online Qzone profile. Gratifications that Chinese adolescents receive from use of the online social network Qzone, such as socializing, information-seeking, and entertainment are found to have a significantly positive influence on their positive mood. Findings of this study extend the existing theoretical framework on the application of the Uses and Gratifications Theory to social networking sites. In addition, findings are in line with those of a number of authors who suggest that social networking site use may have positive consequences for teenagers. Theoretical and practical implications are discussed.     

Kicking out the trolls – Antecedents of social exclusion intentions in Facebook groups

When contributing to groups on social networking sites (SNS), not all group members necessarily adhere to the group's norms (i.e., its explicit or implicit rules); in other words, they show norm-deviant behavior. Despite the popularity of groups on SNS and the frequency of norm-deviant behavior within them, research has to date rarely investigated how members of online groups react to those behaviors. This article introduces a model based on the social identity approach regarding the responses to norm-deviations in Facebook groups and reports an experiment testing this model. In this experiment, deviant members were perceived as questioning what the group stands for (i.e., as subverting the group's identity). Perceptions of identity subversion, in turn, motivated group members to derogate and exclude the deviate. Furthermore, participants were found to ignore the deviate's contributions (i.e., not to recall them after reading the group's timeline). Taken together, the results suggest that group members perceive a deviate's behavior as questioning their group's image. This, however, does not seem to lead to a group's decay, but rather promotes a “natural purification” within the group through elimination of negative influences.     

The effect of smartphone use on trends in European adolescents’ excessive Internet use

Despite growing public health concerns data on excessive Internet use (EIU) allowing for comparison between countries and over time are scarce. Our study is the first attempt to evaluate the changes and the impact of mobile access on EIU in Europe. It is based on data from 2 surveys of adolescents implemented in 2010 and 2013 in 7 European countries that used identical methodology; a total of 7663 adolescents were recruited by random stratified sampling. The levels of EIU were compared and a regression model was used to assess the impact of smart devices, the number of activities performed online, and the specific online applications. A small but significant increase of EIU levels was observed in Belgium, Denmark, Romania, the UK, and overall. Although smartphone use is a positive predictor of EIU, it is moderated by the increasing number of activities performed online. The increase in levels of EIU is also associated with the daily use of online games and social networking sites.     

Information diffusion through social networks: The case of an online petition

People regularly use online social networks due to their convenience, efficiency, and significant broadcasting power for sharing information. However, the diffusion of information in online social networks is a complex and dynamic process. In this research, we used a case study to examine the diffusion process of an online petition. The spread of petitions in social networks raises various theoretical and practical questions: What is the diffusion rate? What actions can initiators take to speed up the diffusion rate? How does the behavior of sharing between friends influence the diffusion process? How does the number of signatures change over time? In order to address these questions, we used system dynamics modeling to specify and quantify the core mechanisms of petition diffusion online; based on empirical data, we then estimated the resulting dynamic model. The modeling approach provides potential practical insights for those interested in designing petitions and collecting signatures. Model testing and calibration approaches (including the use of empirical methods such as maximum-likelihood estimation, the Akaike information criterion, and likelihood ratio tests) provide additional potential practices for dynamic modelers. Our analysis provides information on the relative strength of push (i.e., sending announcements) and pull (i.e., sharing by signatories) processes and insights about awareness, interest, sharing, reminders, and forgetting mechanisms. Comparing push and pull processes, we found that diffusion is largely a pull process rather than a push process. Moreover, comparing different scenarios, we found that targeting the right population is a potential driver in spreading information (i.e., getting more signatures), such that small investments in targeting the appropriate people have ‘disproportionate’ effects in increasing the total number of signatures. The model is fully documented for further development and replications.     

Community detection in social networks using user frequent pattern mining

Recently, social networking sites are offering a rich resource of heterogeneous data. The analysis of such data can lead to the discovery of unknown information and relations in these networks. The detection of communities including ‘similar’ nodes is a challenging topic in the analysis of social network data, and it has been widely studied in the social networking community in the context of underlying graph structure. Online social networks, in addition to having graph structures, include effective user information within networks. Using this information leads to enhance quality of community discovery. In this study, a method of community discovery is provided. Besides communication among nodes to improve the quality of the discovered communities, content information is used as well. This is a new approach based on frequent patterns and the actions of users on networks, particularly social networking sites where users carry out their preferred activities. The main contributions of proposed method are twofold: First, based on the interests and activities of users on networks, some small communities of similar users are discovered, and then by using social relations, the discovered communities are extended. The F-measure is used to evaluate the results of two real-world datasets (Blogcatalog and Flickr), demonstrating that the proposed method principals to improve the community detection quality.     

Twitter versus Facebook: Exploring the role of narcissism in the motives and usage of different social media platforms

The amount of research on social networking sites (SNS) and narcissism is accumulating quickly requiring greater levels of variable specification and more fine-tuned hypothesis testing to clearly determine the relationships among key variables. The current investigation examines two of the most popular SNS, Facebook and Twitter, formulating hypotheses around the specific features of each site within college and adult samples. Unlike previous research that has focused almost exclusively on SNS usage, we focused on active usage (i.e., SNS content generation) as opposed to passive usage (i.e., SNS consumption) and included reasons for usage as a potential black box in the narcissism to SNS usage relationship. Results suggest that the features of Twitter make tweeting the preferred means of active usage among narcissists in the college sample, but not the adult sample, who prefer Facebook. In fact, we found no significant direct or indirect relationship with active usage on Facebook for the college sample, calling into question popular press articles linking Millennial narcissism with Facebook use. Additionally platform differences (i.e., microblogging versus profile-based) may explain the importance of active usage on Twitter relative to Facebook. That is, with Twitter, narcissistic motives for usage all manifest through tweeting while Facebook provides other mechanisms to achieve narcissistic motives.     

Less effortful thinking leads to more social networking? The associations between the use of social network sites and personality traits

Social network sites (SNS) have become an important social milieu that enables interpersonal communication by allowing users to share and create information. This study explored the associations between SNS use and personality traits, i.e., need for cognition (NFC) and information and communication technology (ICT) innovativeness. The findings showed that the SNS use had a negative association with NFC and a positive association with ICT innovativeness. Specifically, people who were more likely to engage in effortful thinking used SNS less often, and those who were high in ICT innovativeness used SNS more often. Meanwhile, those who spent more time on SNS were more likely to be multitaskers. Additionally, those who spent more time on SNS also spent more Internet time in general, more online time for study/work and more time in surfing the Web with no specific purpose. This study, which could be a first look at the link between social networking and our thinking, provides evidence for the associations between social media use and personality traits.     

The Impact of Cross-Addiction on Information Sharing Behaviors on Social Networking Sites

ABSTRACT

There is a rise in the interest among researchers to understand how addiction to social networking sites (SNSs) influences the use of SNS for information sharing. Much less attention has been placed on the interactive effect of multiple addictions (i.e., cross-addiction) on information sharing behaviors on SNSs. In this study, we examine the interaction effect of SNS addiction and alcohol use on two characteristic sharing behaviors on SNSs, self-promotion, and peer promotion of alcohol use. This study contributes to theory and practice as it builds a model that integrates social cognitive theory and normative social influence to explain mechanisms through which cross-addiction shapes information sharing behaviors on SNSs. The proposed model is estimated using data from a sample of college students in a North American university. The results support the theorized relationships between cross-addiction and SNS use to promote addiction-related activities. Implications of the findings for theory and practice are discussed.