Analysis of Home Location Estimation with Iteration on Twitter Following Relationship

User's home locations are used by numerous social media applications, such as social media analysis. However, since the user's home location is not generally open to the public, many researchers have been attempting to develop a more accurate home location estimation. A social network that expresses relationships between users is used to estimate the users' home locations. The network-based home location estimation method with iteration, which propagates the estimated locations, is used to estimate more users' home locations. In this study, we analyze the function of network-based home location estimation with iteration while using the social network based on following relationships on Twitter. The results indicate that the function that selects the most frequent location among the friends' location has the best accuracy. Our analysis also shows that the 88% of users, who are in the social network based on following relationships, has at least one correct home location within one-hop (friends and friends of friends). According to this characteristic of the social network, we indicate that twice is sufficient for iteration.Comment: The 2016 International Conference on Advanced Informatics: Concepts, Theory and Application (ICAICTA2016

Folks in Folksonomies: Social Link Prediction from Shared Metadata

Web 2.0 applications have attracted a considerable amount of attention because their open-ended nature allows users to create light-weight semantic scaffolding to organize and share content. To date, the interplay of the social and semantic components of social media has been only partially explored. Here we focus on Flickr and Last.fm, two social media systems in which we can relate the tagging activity of the users with an explicit representation of their social network. We show that a substantial level of local lexical and topical alignment is observable among users who lie close to each other in the social network. We introduce a null model that preserves user activity while removing local correlations, allowing us to disentangle the actual local alignment between users from statistical effects due to the assortative mixing of user activity and centrality in the social network. This analysis suggests that users with similar topical interests are more likely to be friends, and therefore semantic similarity measures among users based solely on their annotation metadata should be predictive of social links. We test this hypothesis on the Last.fm data set, confirming that the social network constructed from semantic similarity captures actual friendship more accurately than Last.fm's suggestions based on listening patterns.Comment: http://portal.acm.org/citation.cfm?doid=1718487.171852

Preserving Social Media: the Problem of Access

As the applications and services made possible through Web 2.0 continue to proliferate and influence the way individuals exchange information, the landscape of social science research, as well as research in the humanities and the arts, has the potential to change dramatically and to be enriched by a wealth of new, user-generated data. In response to this phenomenon, the UK Data Service have commissioned the Digital Preservation Coalition to undertake a 12-month study into the preservation of social media as part of the ‘Big Data Network’ programme funded by the Economic and Social Research Council (ESRC). The larger study focuses on the potential uses and accompanying challenges of data generated by social networking applications. This paper, ‘Preserving Social Media: the Problem of Access’, comprises an excerpt of that longer study, allowing the authors a space to explore in closer detail the issue of making social media archives accessible to researchers and students now and in the future. To do this, the paper addresses use cases that demonstrate the potential value of social media to academic social science. Furthermore, it examines how researchers and collecting institutions acquire and preserve social media data within a context of curatorial and legislative restrictions that may prove an even greater obstacle to access than any technical restrictions. Based on analysis of these obstacles, it will examine existing methods of curating and preserving social media archives, and second, make some recommendations for how collecting institutions might approach the long-term preservation of social media in a way that protects the individuals represented in the data and complies with the conditions of third party platforms. With the understanding that web-based communication technologies will continue to evolve, this paper will focus on the overarching properties of social media, analysing and comparing current methods of curation and preservation that provide sustainable solutions

Semantic Text Analysis on Social Networks and Data Processing: Review and Future Directions

Social network usage is growing exponentially in the most up-to-date decade; though social networks are becoming increasingly popular every day, many users are continuously active social network users. Using Twitter, LinkedIn, Facebook, and other social media sites has become the most convenient way for people. There is an enormous quantity of data produced by users of social networks. The most common part of modern research analysis is instrumental for many social network analysis applications. However, people actively utilize social networking sites and diverse uses of these sites. social media sites handle an immense amount of knowledge and answer these three computational problems, noise, dynamism, and scale. Semantic comprehension of the document, image, and video exchanged in a social network was also an essential topic in network analysis. Utilizing data processing provides vast datasets such as averages, laws, and patterns to discover practical knowledge. Using social media, data analysis was primarily used for machine learning, analysis, information extraction, statistical modelling, data preprocessing, and data interpretation processes. This research intentions to deliver an inclusive overview of social network research and application analyze state-of-the-art social media data analysis methods by reviewing basic concepts, social networks and elements social network research is linked to. Semantic ways of manipulating text in social networks are then clarified, and literature discusses studies before on these themes. Next, the evolving methods in research on social network analysis are discussed, especially in analyzing semantic text on social networks. Finally, subjects and opportunities for future research directions are explained

Utilization of Social Network Analysis (SNA) in determining The Most Popular Driver Partner App Brands To maximize sales of home-made culinary SMEs

The development of digital and information technology in Indonesia is increasing very rapidly, so this has triggered the development of driver-partner applications and social media in Indonesia. No exception to food SMEs, food SMEs can shift by utilizing the development of driver-partner applications and social media to expand the market segment by distributing information online. In Twitter's social media information, users disclose information that is known to be associated with a brand. This proves the level of user awareness of a brand. Information from these users is User Generated Content (UGC), namely the track records left by users on social media. This study uses this phenomenon to measure the most popular brands on social media to measure one's awareness and interest in an online driver-partner brand. Utilization of analysis of social media users on Twitter social media using Social Network Analysis (SNA) helps food SMEs in assessing the position of driver-partner applications based on the level of public awareness on Twitter social media regarding the driver-partner application brand. So, this research will show and determine the most popular brand between the two driver-partner applications. This study uses the SNA method, with secondary data in the form of consumer tweets on Twitter related to GoFood and GrabFood. This type of research is qualitative that aims to describe a result of the phenomenon that occurs. The result of this research is the ranking of the driver-partner application brand based on the level of user awareness on social media Twitter.Keywords: Small and Medium Enterprises; Social Network Analysis; User Generated Conten

To follow or not to follow? How Belgian health journalists use Twitter to monitor potential sources

Digital technology, the internet and mobile media are transforming the journalism and media landscape by influencing the news gathering and sourcing process. The empowering capacities of social media applications may constitute a key element for more balanced news access and “inclusive journalism”. We will build on two contrasting views that dominate the social media sourcing debate. On the one hand, literature shows that journalists of legacy media make use of social media sources to diversify their sourcing network including bottom-up sources such as ordinary citizens. On the other hand, various authors conclude that journalists stick with their old sourcing routines and continue to privilege top-down elite sources such as experts and government officials. In order to contribute to this academic debate we want to clarify the Twitter practices of professional Belgian health journalists in terms of how they use the platform to monitor potential sources. Therefore, we examined the 1146 Twitter “followings” of six Belgian health journalists by means of digital methods and social network analysis. Results show that top-down actors are overrepresented in the “following” networks and that Twitter’s “following” function is not used to reach out to bottom-up actors. In the overall network, we found that the health journalists mainly use Twitter as a “press club” (Rupar, 2015) to monitor media actors. If we zoom in specifically on the “following” network of the health-related actors, we found that media actors are still important, but experts become the most followed group. Our findings also underwrite the “power law” or “long tail” distribution of social network sites as very few actors take a central position in the “following” lists while the large majority of actors are not systematically monitored by the journalists

Reading the Source Code of Social Ties

Though online social network research has exploded during the past years, not much thought has been given to the exploration of the nature of social links. Online interactions have been interpreted as indicative of one social process or another (e.g., status exchange or trust), often with little systematic justification regarding the relation between observed data and theoretical concept. Our research aims to breach this gap in computational social science by proposing an unsupervised, parameter-free method to discover, with high accuracy, the fundamental domains of interaction occurring in social networks. By applying this method on two online datasets different by scope and type of interaction (aNobii and Flickr) we observe the spontaneous emergence of three domains of interaction representing the exchange of status, knowledge and social support. By finding significant relations between the domains of interaction and classic social network analysis issues (e.g., tie strength, dyadic interaction over time) we show how the network of interactions induced by the extracted domains can be used as a starting point for more nuanced analysis of online social data that may one day incorporate the normative grammar of social interaction. Our methods finds applications in online social media services ranging from recommendation to visual link summarization.Comment: 10 pages, 8 figures, Proceedings of the 2014 ACM conference on Web (WebSci'14