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The topics on Quora allows the users to group questions of similar interest. Social Q&A sites allow users to post questions. These quantities encoding such linguistic activities can be easily measured for each question post. We derive various properties from the question texts and observe the differences between the topic merge and non-merge pairs. 2,42124212,4212 , 421) topic non-merge pairs. Non merge pairs is presented in Figure 3g. We can observe that merge pairs tend to have lower average distance as compared to non-merge pairs. Meanwhile, when people add questions, they tag these with relevant topics so that the question can be channeled to answerers who have relevant expertise, as well as to those who want to learn more about the matter. We have observed in the previous section that these linguistic as well as psycholinguistic aspects of the question asker are discriminatory factors. People who are permanently protected, like the president and first lady, have "details," or groups of agents who are assigned to them. The agents also successfully protected two major events being held simultaneously. It was impressive to know the government goes through such extensive measures to ensure its Secret Service agents are honest. The Secret Service was created in 1865 to combat counterfeiting and fraud.
A newly created topic name may represent the concept better than the existing topic name. These conventions might be created at different time periods. Open questions, on the other hand, contains more sexual, body, health related words which might be reasons why they do not attract answers. Number of answers before merge: Similar to the number of questions, number of answers in a topic is also an engagement indicator and might decide the direction of the merge. When a user decides to merge two topics, he/she has to make a choice about the direction of merge. An important future direction would be to conduct a detailed error analysis (?; ?) of the false positives obtained from our results which hale be helpful in further improving the model. However, these two topics were indeed later merged on 19 Jan 2017. Another such interesting example that we found is ‘Psycho-2’ merged into ‘Psychology-of-Everyday-Life’ on 22 Feb 2017. Automatic enumeration of such correct ‘false positives’ would be an immediate future step. Figure 3c shows the distribution of the unweighted overlap coefficients of the co-occurring topics.
Figure 3d shows the distribution of the co-occurring topics’ parent-child overlap (weighted). Figure 3e shows the distribution. Figure 7: Activity. Familiarity details of the users. According to the style mavens at Real Simple magazine, the fundamentals of figure flattery still apply. Five, say Real Simple editors, "is bordering on clown costume." If you opt for similar shades, go with the same color "intensity," pairing brights with brights, and the like. Williams, Krisztina. "How to Color Block Like A Pro." The Accessorizer/Squidoo. In almost all such cases, users (or Quora moderators) manually merge the topic pair into one of the either topics, thus selecting one of the competing conventions. This early prediction scheme can be easily integrated to the Quora system thus enabling users and moderators to identify and correctly merge topics much early in time. On the other hand, the human judgment wrongly predicted that these two topics should merge. Apart from this, our system was able to correctly predict 37.24% of the cases that were wrongly predicted by the human judgment.
The 180 negative cases consists of 90 unmerge and 90 neighborhood pairs. From the figure, we can clearly observe that this hypothesis is true for the merge pairs. Therefore, such an experiment can be done only for a small set of questions and it would be practically impossible to scale it up for the entire collection of posts on the Q&A site. For OCSVM, we set the kernel as ‘rbf’. Co-occurring topic overlap: Each topic is associated with a set of questions. In case of Random Forest (RF) we set the number of tree as 10. For SVM, we user Linear Kernal and C as 1.0. For LR, we use ‘l2’ penalty with C as 1.0 and set ‘liblinear’ as the optimization algorithm. We find that in case of Quora, 33% of the times an existing convention gets replaced by a new convention. First, to understand how active they are on Quora, they were asked, “How much time (in hours) do you spend on Quora? In this study we focus on the answerability of questions on Quora, i.e., whether a posted question shall eventually get answered. Conte nt h as been generated with the help of GSA C ontent Generator DEMO.