Time Based Feedback and Query Expansion for Twitter Search

Kumar, Naveen; Carterette, Benjamin

doi:10.1007/978-3-642-36973-5_72

Cited by 7 publications

(5 citation statements)

References 2 publications

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“…Some other recent applications of QE are plagiarism detection [203], event search [89,15,43], text classification [269], patent retrieval [180,181,268], dynamic process in IoT [122,123], classification of e-commerce [128], biomedical IR [1], enterprise search [174], code search [205], parallel computing in IR [179] and twitter search [151,304]. Table 7 summarizes some of the prominent and recent applications of QE in literature based on the above discussion.…”

Section: Other Applicationsmentioning

confidence: 99%

Query expansion techniques for information retrieval: A survey

Azad

Deepak

2019

Information Processing & Management

228

104

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With the ever increasing size of the web, relevant information extraction on the Internet with a query formed by a few keywords has become a big challenge. Query Expansion (QE) plays a crucial role in improving searches on the Internet. Here, the user's initial query is reformulated by adding additional meaningful terms with similar significance. QE -as part of information retrieval (IR) -has long attracted researchers' attention. It has become very influential in the field of personalized social document, question answering, cross-language IR, information filtering and multimedia IR. Research in QE has gained further prominence because of IR dedicated conferences such as TREC (Text Information Retrieval Conference) and CLEF (Conference and Labs of the Evaluation Forum). This paper surveys QE techniques in IR from 1960 to 2017 with respect to core techniques, data sources used, weighting and ranking methodologies, user participation and applications -bringing out similarities and differences.There is a huge amount of data available on the Internet, and it is growing exponentially. This unconstrained information-growth has not been accompanied by a corresponding technical advancement in the approaches for extracting relevant information [191]. Often, a web-search does not yield relevant results. There are multiple reasons for this. First, the keywords submitted by the user can be related to multiple topics; as a result, the search results are not focused on the topic of interest. Second, the query can be too short to capture appropriately what the user is looking for. This can happen just as a matter of habit (e.g., the average size of a web search is 2.4 words [257,255]). Third, the user is often not sure about what he is looking for until he sees the results. Even if the user knows what he is searching for, he does not know how to formulate an appropriate query (navigational queries are exceptions to this [51]). QE plays an important part in fetching relevant results in the above cases. Most web queries fall under the following three fundamental categories [51, 139] :-Informational Queries: Queries that cover a broad topic (e.g., India or journals) for which there may be thousands of relevant results. -Navigational Queries: Queries that are looking for specific website or URL (e.g., ISRO).-Transactional Queries: Queries that demonstrate the user's intent to execute a specific activity (e.g., downloading papers or buying books).2 Hiteshwar Kumar Azad, Akshay DeepakCurrently, user-queries are mostly processed using indexes and ontologies, which work on exact matches and are hidden from the users. This leads to the problem of term mismatch: user queries and search index are not based on the same set of terms. This is also known as the vocabulary problem [99]; it results from a combination of synonymy and polysemy. Synonymy refers to multiple words with common meaning, e.g., "buy" and "purchase". Polysemy refers to words with multiple meanings, e.g., "mouse" (a computer device or an animal). Synonymous and polysemou...

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Section: Other Applicationsmentioning

confidence: 99%

Query expansion techniques for information retrieval: A survey

Azad

Deepak

2019

Information Processing & Management

228

104

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“…According to our knowledge, Google's predicting algorithm used for query suggestion displays search queries based on other users' search activities and the contents of Web pages indexed by Google. 10 In addition, Google users might also see search queries from their previous related searches. We suppose that the rest information services involved for comparison (Yahoo!…”

Section: Evaluation Of Results Against Major Search Servicesmentioning

confidence: 99%

“…Efron [5] showed that for a Twitter microblog collection, hashtags may be predicted using query expansion techniques; he proposed restricting the added query terms to those candidates that are hashtags, stripping candidates of their leading "#". In another more recent approach, Kumar and Carterette [10] take into account the fact that most existing models for Information Retrieval do not take the very important time aspect into account and focus on Twitter search models; they utilize time-based feedback and a simple query expansion by using highly frequent terms in top tweets as their expanded terms. In another detailed approach by Massoudi et al [14], authors propose an efficient dynamic query expansion model for microblog post-retrieval, utilizing a language modeling approach to search microblog posts by incorporating query expansion and certain "quality indicators" during matching.…”

Section: Query Manipulation Workmentioning

confidence: 99%

Semantic query suggestion using Twitter Entities

et al. 2015

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“…It also might help in identifying relevant time periods with smaller percentages of irrelevant tweets. Our outcomes could be used to enhance ranking retrieval systems and extend the traditional query-likelihood language model [4]. The temporal information is also incorporated into the query expansion process, which is called temporal query-expansion, e.g.…”

Section: Discussionmentioning

confidence: 99%

Popularity-based temporal relevance estimation for micro-blogging retrieval

Alohaly

Teresco

2014

Proceedings of the 2014 ACM Southeast Regional Conference

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Finding relevant information among the vast amounts of data generated continuously by modern micro-blogging platforms has opened new challenges in information retrieval. Recent studies on time-based retrieval have shown that identifying the relevant time periods to be incorporated into the retrieval process is promising; by relevant time period we mean the peak time of a query that satisfies the temporal needs of the user's query. Or in other words, a time period at which the potential to find accurate matches for the query in a set of retrieved documents is relatively high when compared with other time periods. We refer to this as temporal relevance. In this paper, using data collected from Twitter, we propose a new temporal relevance estimation technique based on tracking documents published by the popular users, who have high indegree (i.e., number of followers). In this study we concentrate on queries that are short (one word) and popular, i.e., constantly consumed by micro-bloggers. We choose the simple frequency-based technique to estimate the relevant time period as a baseline against which we evaluate our technique. The results of our technique either match or suggest a better time period as the most relevant one, when compared with the baseline. In fact, for the type of queries in our study, narrowing our focus to the documents published by popular users produces a query-to-documents matching pattern that uncovers some information about temporal relevance that might otherwise be hidden. Also, our matching pattern reflects the nature of the real-world news events that are related to the user's query more so than the baseline, thus revealing the important time frames more clearly.

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Time Based Feedback and Query Expansion for Twitter Search

Cited by 7 publications

References 2 publications

Query expansion techniques for information retrieval: A survey

Query expansion techniques for information retrieval: A survey

Semantic query suggestion using Twitter Entities

Popularity-based temporal relevance estimation for micro-blogging retrieval

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