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Journal Cover   Journal of Information Science
  [SJR: 1.008]   [H-I: 40]   [837 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0165-5515 - ISSN (Online) 1741-6485
   Published by Sage Publications Homepage  [833 journals]
  • Guest editorial: Recent advances on big social data
    • Authors: Jung J. J.
      Pages: 749 - 750
      PubDate: 2015-11-20T02:14:24-08:00
      DOI: 10.1177/0165551515604550
      Issue No: Vol. 41, No. 6 (2015)
  • A new understanding of friendships in space: Complex networks meet Twitter
    • Authors: Shin, W.-Y; Singh, B. C, Cho, J, Everett, A. M.
      Pages: 751 - 764
      Abstract: Studies on friendships in online social networks involving geographic distance have so far relied on the city location provided in users’ profiles. Consequently, most of the research on friendships has provided accuracy at the city level, at best, to designate a user’s location. This study analyses a Twitter dataset because it provides the exact geographic distance between corresponding users. We start by introducing a strong definition of ‘friend’ on Twitter (i.e. a definition of bidirectional friendship), requiring bidirectional communication. Next, we utilize geo-tagged mentions delivered by users to determine their locations, where ‘@username’ is contained anywhere in the body of tweets. To provide analysis results, we first introduce a friend-counting algorithm. From the fact that Twitter users are likely to post consecutive tweets in the static mode, we also introduce a two-stage distance-estimation algorithm. As the first of our main contributions, we verify that the number of friends of a particular Twitter user follows a well-known power-law distribution (i.e. a Zipf’s distribution or a Pareto distribution). Our study also provides the following newly discovered friendship degree related to the issue of space: the number of friends according to distance follows a double power-law (i.e. a double Pareto law) distribution, indicating that the probability of befriending a particular Twitter user is significantly reduced beyond a certain geographic distance between users, termed the separation point. Our analysis provides concrete evidence that Twitter can be a useful platform for assigning a more accurate scalar value to the degree of friendship between two users.
      PubDate: 2015-11-20T02:14:24-08:00
      DOI: 10.1177/0165551515600136
      Issue No: Vol. 41, No. 6 (2015)
  • Time-sensitive influence maximization in social networks
    • Authors: Mohammadi, A; Saraee, M, Mirzaei, A.
      Pages: 765 - 778
      Abstract: One of the fundamental issues in social networks is the influence maximization problem, where the goal is to identify a small subset of individuals such that they can trigger the largest number of members in the network. In real-world social networks, the propagation of information from a node to another may incur a certain amount of time delay; moreover, the value of information may decrease over time. So not only the coverage size, but also the propagation speed matters. In this paper, we propose the Time-Sensitive Influence Maximization (TSIM) problem, which takes into account the time dependence of the information value. Considering the time delay aspect, we develop two diffusion models, namely the Delayed Independent Cascade model and the Delayed Linear Threshold model. We show that the TSIM problem is NP-hard under these models but the spread function is monotone and submodular. Thus, a greedy approximation algorithm can achieve a 1 – 1/e approximation ratio. Moreover, we propose two time-sensitive centrality measures and compare their performance with the greedy algorithm. We evaluate our methods on four real-world datasets. Experimental results show that the proposed algorithms outperform existing methods, which ignore the decay of information value over time.
      PubDate: 2015-11-20T02:14:24-08:00
      DOI: 10.1177/0165551515602808
      Issue No: Vol. 41, No. 6 (2015)
  • Knowledge discovery from social media using big data-provided sentiment
           analysis (SoMABiT)
    • Pages: 779 - 798
      Abstract: In today’s competitive business world, being aware of customer needs and market-oriented production is a key success factor for industries. To this aim, the use of efficient analytical algorithms ensures better understanding of customer feedback and improves the next generation of products. Accordingly, the dramatic increase in the use of social media in daily life provides beneficial sources for market analytics. Yet how traditional analytic algorithms and methods can be scaled up for such disparate and multistructured data sources is a major challenge. This paper presents and discusses the technological and scientific focus of SoMABiT as a social media analysis platform using big data technology. Sentiment analysis has been employed in order to discover knowledge from social media. The use of MapReduce and the development of a distributed algorithm towards an integrated platform that can scale for any data volume and provide social media-driven knowledge is the main novelty of the proposed concept in comparison to the state-of-the-art technologies.
      PubDate: 2015-11-20T02:14:24-08:00
      DOI: 10.1177/0165551515602846
      Issue No: Vol. 41, No. 6 (2015)
  • The megaphone of the people? Spanish SentiStrength for real-time
           analysis of political tweets
    • Authors: Vilares, D; Thelwall, M, Alonso, M. A.
      Pages: 799 - 813
      Abstract: Twitter is an important platform for sharing opinions about politicians, parties and political decisions. These opinions can be exploited as a source of information to monitor the impact of politics on society. This article analyses the sentiment of 2,704,523 tweets referring to Spanish politicians and parties from a month in 2014–2015. The article makes three specific contributions: (a) enriching SentiStrength, a fast unsupervised sentiment strength detection system, for Spanish political tweeting; (b) analysing how linguistic phenomena such as negation, idioms and character duplication influence Spanish sentiment strength detection accuracy; and (c) analysing Spanish political tweets to rank political leaders, parties and personalities for popularity. Sentiment in Twitter for key politicians broadly reflects the main official polls for popularity but not for voting intention. In addition, the data suggests that the primary role of Twitter in politics is to select and amplify political events published by traditional media.
      PubDate: 2015-11-20T02:14:24-08:00
      DOI: 10.1177/0165551515598926
      Issue No: Vol. 41, No. 6 (2015)
  • Recommendations based on personalized tendency for different aspects of
           influences in social media
    • Authors: Lai, C.-H; Liu, D.-R, Liu, M.-L.
      Pages: 814 - 829
      Abstract: Among the applications of Web 2.0, social networking sites continue to proliferate and the volume of content keeps growing; as a result, information overload causes difficulty for users attempting to choose useful and relevant information. To resolve this problem, most researches only utilize users’ preferences, the content of items or social influence to make recommendations. However, people’s preferences for items may be affected by social friends, personal interest and item popularity. Moreover, each factor has a different impact on each user. In this work, we propose a novel recommendation method based on different types of influences: social, interest and popularity, using personal tendencies in regard to these factors to recommend photos in a photo-sharing website, Flickr. The personal tendencies related to these three influences are regarded as personalized weights to combine influence scores for predicting the scores of items. The experimental results show that our proposed methods can improve the quality of recommendations.
      PubDate: 2015-11-20T02:14:24-08:00
      DOI: 10.1177/0165551515603324
      Issue No: Vol. 41, No. 6 (2015)
  • Where to go and what to play: Towards summarizing popular information from
           massive tourism blogs
    • Authors: Xu, H; Yuan, H, Ma, B, Qian, Y.
      Pages: 830 - 854
      Abstract: In this work, we propose a novel method to summarize popular information from massive tourism blog data. First, we crawl blog contents and segment them into semantic word vectors separately. Then, we select the geographical terms in each word vector into a corresponding geographical term vector and present a new method to explore hot tourism locations and, in particular, their frequent sequential relations from a set of geographical term vectors. Third, we propose a novel word vector subdividing method to collect local features for each hot location, and introduce the metric of max-confidence to identify the Things of Interest (ToI) associated with the location from the collected data. We illustrate the benefits of this approach by applying it to a Chinese online tourism blog dataset. The experimental results show that the proposed method can be used to explore hot locations, as well as their sequential relations and corresponding ToI, efficiently.
      PubDate: 2015-11-20T02:14:24-08:00
      DOI: 10.1177/0165551515603323
      Issue No: Vol. 41, No. 6 (2015)
  • A survey of location inference techniques on Twitter
    • Authors: Ajao, O; Hong, J, Liu, W.
      Pages: 855 - 864
      Abstract: The increasing popularity of the social networking service, Twitter, has made it more involved in day-to-day communications, strengthening social relationships and information dissemination. Conversations on Twitter are now being explored as indicators within early warning systems to alert of imminent natural disasters such as earthquakes and aid prompt emergency responses to crime. Producers are privileged to have limitless access to market perception from consumer comments on social media and microblogs. Targeted advertising can be made more effective based on user profile information such as demography, interests and location. While these applications have proven beneficial, the ability to effectively infer the location of Twitter users has even more immense value. However, accurately identifying where a message originated from or an author’s location remains a challenge, thus essentially driving research in that regard. In this paper, we survey a range of techniques applied to infer the location of Twitter users from inception to state of the art. We find significant improvements over time in the granularity levels and better accuracy with results driven by refinements to algorithms and inclusion of more spatial features.
      PubDate: 2015-11-20T02:14:24-08:00
      DOI: 10.1177/0165551515602847
      Issue No: Vol. 41, No. 6 (2015)
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