Hashtags have become a crucial social media tool. The categorization of posts in a simple and informal manner stimulates the dissemination of content through the web. At the same time, it enables users to find messages within a specific topic of their interest. However, the flexibility provided to the user to apply any hashtag carries some problems. Equivalent expressions, like synonyms, are handled like entirely different words, while the same hashtag may refer to distinct topics. Also, many hashtags are dynamic in the sense their meaning and connections with different subjects change through time and location. This factors may hinder content discovery, specially when discussing less popular subjects. One way to overcome this problem is to provide utilities to identify relevant hashtags. Some research in hashtag recommendation in Twitter has been conducted over recent years but with greater focus on proposing hashtags for new posts instead of for a topic in general. Additionally, most of the current approaches rely on databases which require time to be assembled and rigorous maintenance to keep updated.The approach we propose for the identification of topic relevant hashtags is the development of a method to search Twitter, in real time, for hashtags relevant to a topic and represent them in a graph. For this task, we first retrieve tweets within some degrees of connection with the subject. Next, we employ Latent Dirichlet Allocation and Support Vector Machines, to classify tweets and collect their hashtags relevant to the subject. Finally, we use these hashtags to assemble a network of relations that can be used to deepen content retrieval on the original subject. This approach takes into account factors such as the popularity of the hashtags and current meaning.Furthermore, we analyze the proposed algorithm, both qualitatively and quantitatively, and compare it with past approaches, in order to evaluate its performance. The outcomes of our method are usually equal or superior to the available alternatives, in relation to the number of returned hashtags, and current relevance to the topic. However, our process is by default significantly slower than the existing alternatives.

<abstract> <p>The expansion of cities contributed to the problems related to the accumulation of waste and lack of control over its management, there are still around 2400 dumps or uncontrolled landfills in Brazil. There is a large volume of polyethylene terephthalate (PET) improperly discarded. In turn, the construction industry has been looking for sustainable ways to produce concrete. This work deals with the analysis of the replacement of PET as a fine aggregate in concrete in the proportions of 5% and 15%. PET particles pass more than 75% in the 2.36 mm opening sieve and have more than 99% of their particle size retained in the 0.15 mm opening sieve. Concrete properties, compressive strength, tensile strength, water absorption and void ratio were evaluated and compared with the reference mix. In total, 45 specimens cast in concrete were used to complete the experiment. The results obtained showed that mixture compositions that incorporate PET as fine aggregates decrease compressive and tensile strength, increase water absorption and void index. The results obtained showed that blending compositions that incorporate PET as fine aggregates decrease compressive strength in about 14%, decrease tensile strength in about 7–11%, increased the void ratio in almost 20% and increased the water absorption in about 30%.</p> </abstract>

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Determinação numérica e experimental da frequência natural de vigas de concreto armado Numerical and experimental determination of the natural frequency of armed concrete beams