Probably the only reason you would need to create a new one nowadays would be for the group activities category I mentioned above. For example:. Have a great tech tip that could help others? Add T4Ltip to your tweet so I can share with our community. Retweets count! A Google search led me to believe it stood for Time for Learning.
Probably a great program but still… Bummer. That tweet is long gone but the hashtag is now used for all sorts of other tweets, so I gave up on it. This is probably the most common question I get about hashtags. There are two possible issues here. However, in most cases, this is NOT the problem. This Help article explains some of the factors that may be affecting you as well. But the most important factor is the phrase at the beginning of the first sentence:.
From the same article:. Note: Twitter search intends to bring you closer to content most relevant to you. Our results are refined to combat spam and increase relevance to provide the best possible search experience. Our Support team is unable to force individual Tweets into search. Learn more about using hashtags on Twitter. Their comparison result showed that the hybrid model performed slightly better than using only one model. Our method, however, delves into the impact of retweets as they can be identical to a target tweet and the hashtags in retweets often receive the highest score.
In our method, we removed retweets from the training set so that the recommendation results do not get affected by retweets. Lu et al. They estimate the topic mix of given tweets based on words and time stamp, that determine the distribution of words in the tweet, and recommend words with high probabilities of occurring in the target tweet as hashtags.
The main focus of their model is to capture timely topics rather than performance improvements in terms of computational speed, while one of our goals is the ability to quickly work with a large Twitter data set. The objective of this paper was to implement an effective hashtag recommendation system that automatically suggests a list of personalized hashtags emerging real-time for Twitter users. Inspired by classic information retrieval approaches, we proposed the use of an inverted-index data structure to store two frequency maps that are be built prior to performing the hashtag ranking.
We showed a Map-Reduce-based algorithm to scalably build these inverted-indices over large Twitter data sets. Our experiments on a large Twitter data set demonstrated that our proposed method performed better than other methods that rely only on hashtag popularity and tweet similarity. Finally, we conducted experiments on the top 10 high-scored hashtags. Compared with a ranking method based on cosine similarity, the experiments exhibited that our system consistently assigned high score on hashtags that interests the user.
While our research has demonstrated promising results on recommending personalized hashtags, the scope of the research can be extended in several other directions in the future.
We discuss the most prominent. There exist several studies on sentiment analysis for the domain of microblogs. Some previous efforts show sentiment analysis on the whole tweet [ 28 , 29 ]. Zhang et al. We could exploit this analysis so that entities with positive sentiment have a greater impact on the hashtag recommendations than entities with negative or no sentiment.
Liu et al. These synergistic methods could also be used for hashtag recommendation as in our current work. Even though collaborative filtering increases computational complexity, it may dramatically improve recommendations when an individual user has relatively few posts as is the case with information seekers. Finally, we note that hashtag recommendation may be relevant for more use-cases than our work has so far explored. This could be done, for example, by limiting recommendations to the set of hashtags that a user has previously applied to her tweets.
Schreiner T. New compete study: primary mobile users on twitter. Heggestuen J. One in every 5 people in the world own a smartphone, one in every 17 own a tablet. Tsukayama H. Twitter turns 7: Users send over million tweets per day.
Short text classification in twitter to improve information filtering. New York: ACM. Towards tagging and categorization for micro-blogs. Using topic models for twitter hashtag recommendation.
Recommending -tags in twitter. Jones KS. A statistical interpretation of term specificity and its application in retrieval. J Doc. Article Google Scholar. Dean J, Ghemawat S. Mapreduce: simplified data processing on large clusters. A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. Similarity search in high dimensions via hashing.
Muja M, Lowe DG. Scalable nearest neighbor algorithms for high dimensional data. Tweets twitter collection. On recommending hashtags in twitter networks. In Social Informatics. Berlin: Springer; Stelzner MA. Social media marketing industry report. Social Media Examiner; Automatic generation of personalized annotation tags for twitter users.
Stroudsburg: Association for Computational Linguistics; Mihalcea R, Tarau P. Textrank: Bringing order into texts. In: Lin D, Wu D, editors. Barcelona: Association for Computational Linguistics; Michelson M, Macskassy SA. Pennacchiotti M, Popescu AM. A machine learning approach to twitter user classification. Detecting spammers on twitter.
McCord M, Chuah M. Spam detection on twitter using traditional classifiers. Detecting spam in a twitter network. First Monday. Influence and passivity in social media. In Machine learning and knowledge discovery in databases.
Efron M. Hashtag retrieval in a microblogging environment. New York: ACM; Dovgopol R, Nohelty M. Twitter hash tag recommendation. CoRR; Lu H, Lee C. A twitter hashtag recommendation model that accommodates for temporal clustering effects. Twitter sentiment analysis. Entropy, vol. Wasserman T. Combining lexicon and learning based approaches for concept-level sentiment analysis. Personalized news recommendation based on click behavior. Download references. EO carried out the implementation of the system over Hadoop, e.
All authors have given approval for the publication of this paper. All authors read and approved the final manuscript. You can also search for this author in PubMed Google Scholar. Correspondence to David Chiu. Reprints and Permissions. Otsuka, E. A hashtag recommendation system for twitter data streams. Compu Social Networls 3, 3 Download citation. Received : 07 May Accepted : 12 May Published : 31 May Anyone you share the following link with will be able to read this content:.
Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all SpringerOpen articles Search. Download PDF. Abstract Background Twitter has evolved into a powerful communication and information sharing tool used by millions of people around the world to post what is happening now.
Background Micro-blogging has become a popular communication and information search tool, and Twitter is one of the most prevalent micro-blogging platforms today, with over million active users posting tweets , a message limited to characters [ 1 ]. Background This section introduces the terminologies that are used to address the services and features of Twitter.
Index generation and ranking Our hashtag ranking algorithm is inspired by the well-known TF-IDF [ 8 ] approach used in information retrieval. Full size image.
Map-Reduce—Map phase. Map-Reduce—Shuffle phase. Map-Reduce—Reduce phase. Experimental evaluation In this section, we present a nuanced evaluation of our system. Tweet corpus To evaluate our hashtag recommendation system, we first obtained the Tweets corpus, consisting of a collection of tweet identifiers, provided by Twitter for the TREC Microblog Track [ 13 ]. Downloaded Tweets. Table 1 Overview of data characterization Full size table. Table 2 Top 30 popular hashtags Full size table.
Example measurement of precision. Example measurement of recall. Recall for the ranking methods. Precision for the ranking methods. Recall depending on the number of recommended tags ranked with kNN.
Percentage of matched hashtags due to the presence of retweets depending on the ranking method. Table 4 Top 10 recommended hashtags ranked with kNN Full size table. Table 6 Top 10 recommended hashtags not including top 30 most popular hashtags in the data set Full size table. Table 7 Top 10 recommended hashtags ranked by the KNN method, not including top 30 most popular hashtags in the data set Full size table. Map-Reduce vs. Related work As the number of micro-blog users increases, Twitter has become one of the most powerful medium generating millions of free-form tweets per day, and many researchers and industries have conducted extensive analysis of micro-blogs data since Twitter launched in User classification Pennacchiotti et al.
Category recommendation Sriram et al. Hashtag recommendation Most related to our work is the class of hashtag recommendation systems. Name That Thing. Take our visual quiz. Test Your Knowledge ». Learn More ». The Merriam-Webster Unabridged Dictionary. Online access to a legendary resource Log In or Sign Up ». Merriam-Webster's Visual Dictionaries.
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