Social networks form an important platform for information sharing and interaction among users. The content from social networks can be used to generate recommendations for users in order to help them to choose what they desire. There exist a lot of recommendation methods currently. In this paper, we propose a keyword
Social media services have already become main sources for monitoring emerging topics and sensing real-life events. A social media platform manages social stream consisting of a huge volume of timestamped user generated data, including original data and repost data. However, previous research on keyword search over
We examine whether aggregate daily Twitter keyword volumes over eight months from November 2011 to June 2012 can be used to predict aggregate daily consumer spending as reported by Gallup. We also examine whether Twitter keyword volume improves predictive ability over prediction based solely on current spending
social media. Discovering keyword-based correlated networks of these large graphs is an important primitive in data analysis, from which users can pay more attention about their concerned information in the large graph. In this paper, we propose and define the problem of keyword-based correlated network computation over a
propose Term-Frequency and Inverse Document Frequency (TF-IDF) method to rank keywords of top twenty most followed Instagram users based on image captions of Instagram. The objective of this research is to automatically know the main idea of Instagram users based on 50 recent image captions posted. In our experiments, TF-IDF
Being able to automatically extract as much relevant posts as possible from social media in a timely manner is key in many activities, for example to provide useful information in order to rapidly create crisis maps during emergency events. While most social media support keyword-based searches, the amount and the
localized events such as sport events, demonstrations, or traffic jams, to name but a few. The main building blocks of a localized event are local keywords that exhibit a surge in usage at the event location. In this paper, we propose an approach that aims at extracting local keywords from a stream of Twitter messages by (1
audiences and website's competitors when analyzing keywords; (2) insert keywords into web text that will appear on search engine results pages, and (3) involve their web content and websites with other web content creators. Implications: Because successful search engine optimization requires considerable time, professional
perspective of an attraction. It has become a hot topic about how to discover scenic from the scattered pictures and then provide users with panoramic display of the tourist attractions. In this paper, we presents a novel method to discover hot spots of a given area based on social media data, and show them extracted keywords
Social media keeps growing and providing us with rich sources of information to understand our everyday lives, customs, and culture in the form of periodic topics. This paper proposes a method of detecting periodic topics based on autocorrelation using the time series of the document frequencies of keywords. To deal
of that unrest on Phuket's tourism environment. It is proposed that this analysis can provide measurable insights through summarization, keyword analysis and clustering. We measure sentiment using a binary choice keyword algorithm. A multi-knowledge based approach is proposed using, Self-Organizing Maps along with
part of a trending discussion topic by the presence of a tagged keyword. Relying solely on this keyword, however, may be inadequate for identifying all the discussion associated with a trend. Our research demonstrates that machine learning techniques can be used identify the top trend a tweet belongs to with up to 85
their historical and social context by understanding how the major topics associated with them have changed over time. Users can relate articles through time by examining the topical keywords that summarize a specific news event. By tracking the attention to a news article in the form of references in social media (such as
Bad news travels fast. Although this concept may be intuitively accepted, there has been little evidence to confirm that the propagation of bad news differs from that of good news. In this paper, we examine the effect of user perspective on his or her sharing of a controversial news story. Social media not only offers insight into human behavior but has also developed as a source of news. In this...
Microblogging services allow users to publish their thoughts, activities, and interests in the form of text streams and to share them with others in a social network. A user’s text stream in a microblogging service is temporally composed of the posts the user has written or republished from other socially connected users. In this context, most research on the microblogging service has primarily focused...
With the rapid growth of web, automatic tagging that detects informative terms from a document becomes an important problem for information aggregation and sharing services. In particular, automatic tagging for short documents becomes more interesting as many users are increasingly publishing information through social media services which encourage users to create the documents of short length. In...
Recent years have witnessed an explosive growth of user contributed videos on websites like YouTube and Metacafe, which usually provide a query-by-keyword functionality to facilitate the user browsing. For a given query, the returned videos typically contain multiple topics that are mixed up to duplicate the user
) surveillance. Methods Syndrome definitions were created using keyword categorization to extract posts from Twitter. Categories were developed iteratively for four relevant syndromes: respiratory, gastrointestinal, heat-related illness, and influenza-like illness (ILI). All data sources corresponded to the location of
interest. The methodologies used to uncover relevant documents range from manually curated keyword filters to trained classification models. Any serious topical analysis requires a sound understanding of key metrics behind the retrieval process, two of the most important being precision and recall. While precision can be
useful features extracted from each Twitter's message. The output is its degree of relevance for each message to Sandy. A number of fuzzy rules are designed and different defuzzification methods are combined in order to obtain desired classification results. We compare the proposed method with the well-known keyword search
Financed by the National Centre for Research and Development under grant No. SP/I/1/77065/10 by the strategic scientific research and experimental development program:
SYNAT - “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.