Popular Social Networking Service Research Articles

Impact of Entrepreneurial Ecosystem Discussions in Smart Cities: Comprehensive Assessment of Social Media Data

Discussions on “smart cities” are gaining in popularity in the past two decades and has shown potential in tackling the cities’ environmental, social, and economic challenges. Smart cities are known as a system of physical infrastructure, the information and communications technology (ICT) infrastructure, and the social infrastructure that exchange information that flows between its many different subsystems. The “smart cities” concept has been introduced with various dimensions, among those, the embedded ICT infrastructure in smart cities is playing a decisive role between the functions of the system. One of the important derivatives of ICT is the new communication mediums known as social network services (SNSs), which is emergent and introduces additional functionalities to “smart cities”. This paper seeks to advance the understanding of SNSs in smart cities to evaluate the effects on the innovation and entrepreneurial ecosystem. This agenda has been tackled by a rigorous methodological approach in order to capture and evaluate the presence of entrepreneurially concerned discussions in a popular SNS intermediate (Twitter). Beyond the methodological contribution on handling big data in SNSs for gaining insights on innovation and entrepreneurial aspects in smart cities, the findings distinguished the influence of a certain category of content generators (professionals) that drive the biggest motives of the interactions in SNSs.

Big Data Analyses of Korea's Nation Branding on Google and Facebook

Big Data researchers have typically focused on images, trends, risk management, storytelling tools, and marketing. However, research into nation branding content disseminated through online platforms remains scarce. In this exploratory study, we collected data regarding Korea s nation branding and related material from Google and Facebook (the most widely used search engine and the most popular social networking service, respectively). By semantically analyzing this data, we provide a structural understanding of international perspectives on Korean culture. Online platforms serve as infrastructures that governments use to develop and distribute various applications throughout the world. The link between nation branding and the Korean Wave highlights the need for additional research examining the specific elements of Korea s nation brand and the branding strategies deployed via online platforms.

Protecting Metadata of Access Indicator and Region of Interests for Image Files

With popularity of social network services, the security and privacy issues over shared contents receive many attentions. Besides, multimedia files have additional concerns of copyright violation or illegal usage to share over communication networks. For image file management, JPEG group develops new image file format to enhance security and privacy features. Adopting a box structure with different application markers, new standards for privacy and security provide a concept of replacement substituting a private part of the original image or metadata with an alternative public data. In this paper, we extend data protection features of new JPEG formats to remote access control as a metadata. By keeping location information of access control data as a metadata in image files, the image owner can allow or deny other’s data consumption regardless where the media file is. License issue also can be resolved by applying new access control schemes, and we present how new formats protect commercial image files against unauthorized accesses.

Spatial peculiarities of social networking services distribution in Rivnenska oblast

Social networking services are a modern phenomenon in the field of human relations. As differentiated from western science, the issue of SNS spatial distribution is not a matter of high concern in Ukrainian researchers because of a number of objective and subjective reasons. The main task of the research is to analyze the spatial distribution of the amount of users and the penetration level of social networking services such as Facebook and Instagram in administrative districts and cities of region subordination belonging to Rivnenska oblast. Facebook is the most popular social networking service in Rivnenska oblast. There is a trend towards concentration of users' accounts in big cities: Rivne City, Dubno City, Varash City. More than 70% of users of the oblast live in the cities of oblast subordination and 58.4% of accounts are concentrated in Rivne City which is also the regional center. T. Hägerstrand's Spatial Innovation Diffusion Theory has been proved – the largest cities of the oblast are regional innovation centers. Densely populated Sarnenskyi, Bereznivskyi, and Kostopilskyi districts are distinguished among the districts. Facebook is the least used in Dubenskyi, Rivnenskуі, Ostrozkyi, Zarichnenskyi, Demydivskyi, Hoshchanskyi districts. The penetration rate of the social networking service Facebook in the oblast is 32.7% (in Ukraine – 31.0%). The highest level is in cities of oblast subordination. Rivne City and Dubno City are particularly distinguished. High levels of penetration are achieved due to the high development of technical infrastructure, through the companies' and shops' accounts, as well as accounts of residents from surrounding villages. The relatively low penetration rate in Varash City, the second largest city of Rivne oblast due to population, can be explained by the weaker connections with the surrounding area. In administrative districts, the penetration rate is much lower. A relatively high percentage of users are in Radyvylivskyi, Bereznivskyi, Kostopilskyi districts. These districts are characterized by a high level of socio-economic development and convenient socio-geographical location. The lowest level of penetration of the social networking service is in Demydivskyi, Zarichnenskyi, Hoshchanskyi districts. These are little developed and depressed territories within the oblast. The second most popular social networking service in Rivnenska oblast is Instagram (26.3% penetration rate). In terms of development, it is inferior to Facebook, and its main users are mostly young people. In the spatial distribution of Instagram users, we can trace the same trends as the Facebook. In general, the distribution of social networking services such as Facebook and Instagram in Rivnenska oblast corresponds to a hierarchical model of spatial diffusion. There is a trend towards concentration of users in big cities.

Event Detection in Twitter Using Multi Timing Chained Windows

Twitter is a popular microblogging and social networking service. Twitter posts are continuously generated and well suited for knowledge discovery using different data mining techniques. We present a novel near real-time approach for processing tweets and detecting events. The proposed method, Multi Timing Chained Windows (MTCW), is independent of the language of the tweets. The MTCW defines several Timing Windows and links them to each other like a chain. Indeed, in this chain, the input of the larger window will be the output of the smaller previous one. Using MTCW, the events can be detected over a few minutes. To evaluate this idea, the required dataset has been collected using the Twitter API. The results of evaluations show the accuracy and the effectiveness of our approach compared with other state-of-the-art methods in the event detection in Twitter.

Measuring Time-Sensitive and Topic-Specific Influence in Social Networks With LSTM and Self-Attention

Influence measurement in social networks is vital to various real-world applications, such as online marketing and political campaigns. In this paper, we investigate the problem of measuring time-sensitive and topic-specific influence based on streaming texts and dynamic social networks. A user's influence can change rapidly in response to a new event and vary on different topics. For example, the political influence of Douglas Jones increased dramatically after winning the Alabama special election, and then rapidly decreased after the election week. During the same period, however, Douglas Jones' influence on sports remained low. Most existing approaches can only model the influence based on static social network structures and topic distributions. Furthermore, as popular social networking services embody many features to connect their users, multi-typed interactions make it hard to learn the roles that different interactions play when propagating information. To address these challenges, we propose a Time-sensitive and Topic-specific Influence Measurement (TTIM) method, to jointly model the streaming texts and dynamic social networks. We simulate the influence propagation process with a self-attention mechanism to learn the contributions of different interactions and track the influence dynamics with a matrix-adaptive long short-term memory. To the best of our knowledge, this is the first attempt to measure time-sensitive and topic-specific influence. Furthermore, the TTIM model can be easily adapted to supporting online learning which consumes constant training time on newly arrived data for each timestamp. We comprehensively evaluate the proposed TTIM model on five datasets from Twitter and Reddit. The experimental results demonstrate promising performance compared to the state-of-the-art social influence analysis models and the potential of TTIM in visualizing influence dynamics and topic distribution.

Public Knowledge about Dementia in China: A National WeChat-Based Survey.

Dementia is a serious public health problem. The more extensive dementia knowledge is, the more conducive it is to early prevention and treatment of dementia. However, no assessment of the general population’s dementia awareness has been conducted so far in China. Thus, this study assessed the national public knowledge of dementia based on mobile internet in China. We assessed 10,562 national respondents recruited based on the most popular social networking service in China, WeChat and analyzed the data using quantitative methods. The overall correct rate of total dementia knowledge was 63.14%. Only half of the participants (50.84%) could identify risk factors accurately. The level of dementia knowledge was positively associated with high education, city residency, and experience of exposure to information on dementia. The sandwich generation (aged 20–60 years) had the highest level of dementia knowledge. Chinese people were found to have a low level of knowledge about dementia, especially those aged over 60 years, with low education and living in rural areas. Further educational programs and campaigns are needed to improve dementia knowledge, with greater focus on the older population as the target audience, emphasis on dementia risk factors as educational content, correcting misconceptions about dementia, and providing more experience of exposure to dementia.

Chinese teachers’ imaginaries: comparing the pros and cons of Chinese education and other education systems

ABSTRACT The article presents a qualitative study which explores Chinese school teachers’ imaginaries of Chinese and other education systems. It also investigates how social media has shaped their imaginaries. By reference to the literature on imaginary, discourse, and media, the study views imaginary as an instantiation of a particular discourse or discourses. This research employs qualitative coding methods and textual analysis to interpret the meanings of 163 opinion articles written by part-time graduate students, who were full-time school teachers. These methods were also used to analyse interview and observation transcripts. The findings reveal that most participants adopted stereotypical perspectives of western and Chinese education, and exhibited a strong sense of self-deprecation. Interestingly, WeChat, a popular Social Network Services (SNSs), played a prominent role in constructing these teachers’ imaginaries. The study highlights the hegemonic power given to western education and the critical role played by social media in the Chinese context.

Understanding Factors that Influence User Popularity in Live Streaming Platforms

Live streaming platforms such as Twitch and Periscope have become some of the most popular synchronous social networking services. In order to attract viewers, streamers are motivated to broadcast exciting video content while actively interacting with viewers. The emerging stream of research on the live streaming community has examined the streamers’ motivations and how the viewers react to streamers. However, few studies have focused on understanding the characteristics of popular streamers. Popular streamers create tremendous business value for social media influencer investors, as they have high potential to create persuasive advertisements and endorsements for firms by promoting their products and services. We aim at examining the key characteristics that associate with the streamers’ viewer base, namely their personality, professionalism, and streaming affordance. Based on text mining and analyses of video content, our results show: 1) certain personality traits (such as openness) are negatively associated with both cumulative and current popularity; 2) professional players are more likely to attract a larger viewer base; 3) and the social affordance, including profile building affordance, social connectivity, and social interactivity, is positively associated with both cumulative and current popularity. Our results provide useful insights into measuring and evaluating streamers’ popularity, which in turn generates actionable strategies for social media influencer investors and platform operators.

Improving response capability of chatbot using twitter

One of the most familiar services to smartphone users is mobile messenger. At present, such messenger services are rapidly developing into chatbots, including links with commercial platforms to pursue customer service. A chatbot is not a person but a virtual conversation partner or a conversational robot that can respond to questions through a chatting interface according to predefined response rules in a widely used messaging platform. A chatbot usually has a knowledge database to have conversations with users and provides smart responses by applying various algorithms. However, because it mostly only uses limited dialogue information pre-stored in the knowledge database, the chatbot cannot provide flexible answers when it receives an input question that does not exist in the knowledge database. Therefore, this study proposes a novel technique that can provide accurate and flexible answers to the users of chatbot services by expanding the knowledge database through the use of Twitter, a popular social networking service. To this end, a keyword matching-based answer retrieval technique was studied to provide appropriate answers in terms of context based on a method that can collect the data of dialogues between users from Twitter. To verify the effectiveness of the proposed technique, a comparative analysis with three commercial chatbots, currently used by many people, was performed. Ten evaluators were recruited and the response appropriateness of each chatbot was evaluated with a variety of question sets. As a result, it was concluded that the proposed technique provided the most appropriate answers in terms of context.

Exploring Smartphone Users’ Social Information Behavior

AAs the popularity of social network services (SNS) grows, users’ online behavior is increasingly being affected by SNS browsing. Now, mobile device serve as a tool for social connection, provides an increasingly important communication channel in facilitating social connections. SNSs that are adequate for mobile devices have been developed. To clarify web user social information behavior, this study used social network sites to analyze smartphone user online behavior by collecting user clickstream data over a period of 3 months. The influence of SNS browsing on types of external sites visited—namely news, shopping, searches, forums, videos, and blogs—is examined. This study used a site-centric approach, wherein user data was examined by using association rules and the Jaccard Index to explore the relationship between SNS and other online activities. We collaborates with InsightXplorer to recruit smart phone users, collected 580 samples from the consumers who use the smartphone. First, user-centric approach will be conducted for correlation analysis to quantify the association between SNSs and other categories, and then use the results as the standard to conduct fixed effect model by exploring the duration of gender, age and weekdays/weekend at 24hrs. Second, site-centric approach will conduct correspondence analysis and association rules by using users' session to gather all categories in the graph and explore the relationship between the SNSs and the other categories. The result shows that Taiwanese smartphone users use Facebook (both Web and App) as a news-seeking, topic-seeking, video-seeking and word-of-mouth–seeking platform. That means Facebook will become a portal. When people want to find something interesting, the first thing is to open Facebook and searching their timeline. Finally, based on the findings, this study to provide a stepping stone for marketers and researchers to understand web users’ social information behavior. Keywords: Smartphone, Social information behavior, Web usage mining, Clickstream data, Mobile commerce To cite this document: Chiang, I.-P., Lin, C.-Y., & Wu, Y.-J. (2019). Exploring smartphone users’ social information behavior. Contemporary Management Research, 15(1), 53-67. https://doi.org/10.7903/cmr.18461

중국 제3자 모바일 결제시장에서 후발주자의 추격 성공 요인 분석— 위챗페이의 오락성을 중심으로

China’s third-party mobile payment market is expected to grow at average of over 38% until 2019, following the establishment of Alipay in 2004. Wechat Payment has grown rapidly since 2013 as a latecomer, and has formed a duopoly market structure with Alipay. We analyzed common factors and individual factors that influence the behavioral intention and use behavior of Alipay and Wechat Payment through the modified UTAUT model. We found that usability, ease of acceptance, and ease of use play an important role in the behavioral intention of third party mobile payment service. In particular, hedonic motivation is the key factor behind the rapid growth of Wechat Payment. Tencent exploited ‘sociability’ by combining Wechat Payment with Wichat’s main interface, China’s most popular social networking service (SNS) and targeted a niche market such as financial transactions or free remittances between acquaintances. Most importantly, it held the interesting Red Packet events every year since 2014. As consumers will want to consume more pleasure and amusement in the future, it is necessary to build infrastructures that emphasize amusement aspects in financial services like Wechat Payment.

Examining the Effects of Passive WeChat Use in China

ABSTRACT Passive usage of social network services (SNSs) refers to individuals’ browsing behavior and information consumption without active interaction with other SNS users. Using the data collected from 295 users of WeChat, the most popular SNS in China, the authors identify two types of passive use and test their different consequences. Confirmatory factor analysis in Study 1 shows passive WeChat use includes two subdimensions, namely, passive social and nonsocial use. Passive social use means browsing friends’ posts, whereas passive nonsocial use refers to browsing information on WeChat official accounts. The partial least squares structural equation modeling results in Study 2 suggest that both social and nonsocial use have positive effects on users’ social capital and online well-being, and further increase their continuance usage of SNSs. However, passive social use is also associated with users’ depressed mood, which in turn decreases their continuous usage intention. Theoretical and practical implications are discussed.

Just being there matters

PurposeAlthough user behaviors in social network service (SNS) have been well studied in prior literature, most of these studies focus on those behaviors with relatively deep user engagement such as information disclosure, while the underlying mechanisms that explain users’ shallow engagement behaviors (e.g. Like behavior) have been rarely discussed. To fill this research gap, the purpose of this paper is to propose and empirically test a research model to identify the antecedents of Like behavior.Design/methodology/approachThis study identifies the distinctions between post behavior and Like behavior and develops a research model of Like behavior by emphasizing the role of sense of presence from the perspective of symbolic interactionism. The model is tested through a survey with 479 users of WeChat (a popular SNS tool in China). Structural equation modeling, SmartPLS in particular, is used for data analysis.FindingsThree value perceptions, namely cognitive value, hedonic value and social value, are found to be positively associated with Like intention, and sense of presence is found to affect Like intention both directly and indirectly via the three value perceptions.Research limitations/implicationsThe research model is tested based on a specific SNS in China, so whether the conclusions can be applied to other research contexts should be further examined in future research. This study identifies the distinctions between post behavior and Like behavior and suggests to view the Like behavior from the perspective of symbolic interactionism.Practical implicationsThe paper outlines ways to effectively promote SNS users’ Like behaviors by enhancing the functions related to three value perceptions, especially by enriching the ways that facilitate interpersonal interactions.Originality/valueThis paper is one of the first to distinguish Like behavior from post behavior in SNS, propose and empirically test a research model of Like behavior. In particular, this paper strengthens the important role of sense of presence from the perspective of symbolic interactionism which has rarely been investigated in prior studies.

Examining Gifting Through Social Network Services: A Social Exchange Theory Perspective

The increasing popularity of social network services (SNS) presents an opportunity to offer gifting services through SNS. For givers, gifting can be an important means to enhance social relationships. On the other hand, for SNS providers, members’ gifting can serve as a major source of revenue. As SNS providers continue to face challenges in generating revenues, understanding how to stimulate gifting through SNS can allow them to profit from members’ relationships. However, there is little understanding of what drives members’ gifting through SNS, with limited prior research on online gifting. Thus motivated, we develop a research model of the antecedents of SNS gifting that builds on social exchange theory and prior gifting literature, and incorporates the unique aspects of such gifting (that we refer to as microgifting, with low-price digital voucher gifts). The theoretical model was validated through a field study, in which both subjective and objective data were collected from an SNS that has been successful in offering such gifting services. Our findings highlight the effects of perceived worth, SNS gifting experience, and the number of SNS friends on the frequency of SNS gifting. The results also show that expected benefits (i.e., reciprocity, pleasure, relationship support, convenience, and immediacy of gift sending) and costs (i.e., impersonality) indirectly impact SNS gifting frequency through the assessment of perceived worth. The study contributes to research by adding to our understanding of this new approach of gifting through SNS, i.e., microgifting. It also lends insights on how SNS providers can offer such services to tap this source of revenue.

Optimization of Microblog Representation Using Deep Learning Approach

Microblogging, as a new form of online communication in which users talk about their daily lives, to gather real-time news, opinion about people or share information by short posts, has become one of the most popular social networking services today, e.g. to stay in touch with friends . Finding proper representations of microblog texts is a challenging issue. The overview of microblog included how to extract information from microblog, also discuss about Microblogging and Twitter. This paper included existing research on Optimization of Microblog Representation by using existing techniques and methods as well as microblogging services.Current approaches, for microblog reduction are single indexing. In this work, we have proposed double indexing method for microblog reduction. We have used our own generated dataset for microblog reduction. We have collected tweets from real time twitter for both single and double indexing technique. Also, we have use LDA with Semantic Similarity algorithm. We compared our results with single indexing. Experimental results shows that double indexing gives better performance than single indexing. This may happen because we will show both original and reduced tweets, so it will be known to user which tweet are removed and relevant part shows as output. We have also compared our results with the state-of-art. The proposed double indexing gives better performance than existing 1-ROCA of improved online SVM technique.

Analyzing influence of emotional tweets on user relationships using Naive Bayes and dependency parsing

Twitter is one of the most popular social network services (SNS) applications, in which users can casually post their messages. Given that users can easily post what they feel, Twitter is widely used as a platform to express emotions. These emotional expressions are considered to possibly influence user relationships on Twitter. In our previous study, we analyzed this influence using emotional word dictionaries. However, we could not measure the emotion scores for the words not included in the dictionaries. To solve this problem, in this study, we use the Naive Bayes and consider dependency parsing, i.e., the structure of tweets and the relationships of words. Furthermore, we introduce a set of new measures, namely total positive emotion score (TPES), total negative emotion score (TNES), and total neutral emotion score (TNtES). Based on these measures, we define a new composite index (CI) for emotion scores, which is a normalized value in the range of 0 to 1. We categorize users into positive and negative groups based on the composite index and test the difference of user relationships between these two groups with a statistical method. The result demonstrates that the relationships of positive users not only get better (i.e., the number increases) with time, but also tends to be mutual, which is consistent with the result of our previous study.

Influencing Factors Analysis for a Social Network Web Based Payment Service in China

WeChat payment has recently become a popular mobile payment service in China by bundling with WeChat, the most popular social network service in China. It is interesting to investigate the reasons for its popularity. In this research, we applied the technology acceptance model to predict its acceptability and to identify variables attributing to its popularity. In addition to the primary explanatory variables, i.e., Perceived Ease of Use and Perceived Usefulness, the proposed framework is further extended to include the constructs of Social Interaction, Trust, Perceived Enjoyment and Use Context. The results indicated that the proposed model is able to explain the variance in a user’s intention to use WeChat payment service. We hope this study can provide insights into understanding the adoption behaviour of socially aware mobile payment services.

Spatial-Temporal Event Detection from Geo-Tagged Tweets

As one of the most popular social networking services in the world, Twitter allows users to post messages along with their current geographic locations. Such georeferenced or geo-tagged Twitter datasets can benefit location-based services, targeted advertising and geosocial studies. Our study focused on the detection of small-scale spatial-temporal events and their textual content. First, we used Spatial-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) to spatially-temporally cluster the tweets. Then, the word frequencies were summarized for each cluster and the potential topics were modeled by the Latent Dirichlet Allocation (LDA) algorithm. Using two years of Twitter data from four college cities in the U.S., we were able to determine the spatial-temporal patterns of two known events, two unknown events and one recurring event, which then were further explored and modeled to identify the semantic content about the events. This paper presents our process and recommendations for both finding event-related tweets as well as understanding the spatial-temporal behaviors and semantic natures of the detected events.

Investigating the impact of mobile SNS addiction on individual’s self-rated health

PurposeThe purpose of this paper is to investigate how WeChat addiction influences users’ physical, mental, and social health.Design/methodology/approachA national survey was conducted in China. A total of 1,058 responses were collected from 31 regions of China.FindingsThe regression results show that WeChat addiction is negatively associated with users’ physical, mental, and social health. The negative effects are significant even after adjusting for the effects of the Big Five personality traits, years of using WeChat, and demographic variables such as age, gender, education level, and monthly income. Years of using WeChat is not significantly related to users’ health. It is also found that the influence of WeChat addiction on health outcomes is sensitive to years of WeChat use. The influence is dormant when users have less than three years of WeChat usage, but starts to exhibit itself after three years.Research limitations/implicationsAddictive use of WeChat is associated with declining overall health among Chinese users. Given the cross-sectional nature of this study, definite causal relationship between WeChat addiction and health deterioration cannot be established. Controlled experiments are needed to further examine the causal effects of WeChat addiction.Originality/valueWeChat is the most popular mobile social network service (SNS) in China, but its comprehensive impact on users’ health is rarely studied. This paper extends the extant research on SNS addiction by providing a deepened understanding of how mobile SNS addiction affects personal health in the unique context of WeChat, which provides an important contribution to the interdisciplinary research in public health, psychology, and information systems.