text: The web is trapped in the "perpetual now", and when users traverse from page to page, they are seeing the state of the web resource (i.e., the page) as it exists at the time of the click and not necessarily at the time when the link was made. Thus, a temporal discrepancy can arise between the resource at the time the page author created a link to it and the time when a reader follows the link. This is especially important in the context of social media: the ease of sharing links in a tweet or Facebook post allows many people to author web content, but the space constraints combined with poor awareness by authors often prevents sufficient context from being generated to determine the intent of the post. If the links are clicked as soon as they are shared, the temporal distance between sharing and clicking is so small that there is little to no difference in content. However, not all clicks occur immediately, and a delay of days or even hours can result in reading something other than what the author intended. We introduce the concept of a user's temporal intention upon publishing a link in social media. We investigate the features that could be extracted from the post, the linked resource, and the patterns of social dissemination to model this user intention. Finally, we analyze the historical integrity of the shared resources in social media across time. In other words, how much is the knowledge of the author's intent beneficial in maintaining the consistency of the story being told through social posts and in enriching the archived content coverage and depth of vulnerable resources?
  @cite_30: Crowdsourcing has recently become popular among machine learning researchers and social scientists as an effective way to collect large-scale experimental data from distributed workers. To extract useful information from the cheap but potentially unreliable answers to tasks, a key problem is to identify reliable workers as well as unambiguous tasks. Although for objective tasks that have one correct answer per task, previous works can estimate worker reliability and task clarity based on the single gold standard assumption, for tasks that are subjective and accept multiple reasonable answers that workers may be grouped into, a phenomenon called schools of thought, existing models cannot be trivially applied. In this work, we present a statistical model to estimate worker reliability and task clarity without resorting to the single gold standard assumption. This is instantiated by explicitly characterizing the grouping behavior to form schools of thought with a rank-1 factorization of a worker-task groupsize matrix. Instead of performing an intermediate inference step, which can be expensive and unstable, we present an algorithm to analytically compute the sizes of different groups. We perform extensive empirical studies on real data collected from Amazon Mechanical Turk. Our method discovers the schools of thought, shows reasonable estimation of worker reliability and task clarity, and is robust to hyperparameter changes. Furthermore, our estimated worker reliability can be used to improve the gold standard prediction for objective tasks.
  @cite_31: Many web documents are dynamic, with content changing in varying amounts at varying frequencies. However, current document search algorithms have a static view of the document content, with only a single version of the document in the index at any point in time. In this paper, we present the first published analysis of using the temporal dynamics of document content to improve relevance ranking. We show that there is a strong relationship between the amount and frequency of content change and relevance. We develop a novel probabilistic document ranking algorithm that allows differential weighting of terms based on their temporal characteristics. By leveraging such content dynamics we show significant performance improvements for navigational queries.
  @cite_2: Mood is an important access point in music digital libraries and online music repositories, but generating ground truth for evaluating various music mood classification algorithms is a challenging problem. This is because collecting enough human judgments is time-consuming and costly due to the subjectivity of music mood. In this study, we explore the viability of crowdsourcing music mood classification judgments using Amazon Mechanical Turk (MTurk). Specifically, we compare the mood classification judgments collected for the annual Music Information Retrieval Evaluation eXchange (MIREX) with judgments collected using MTurk. Our data show that the overall distribution of mood clusters and agreement rates from MIREX and MTurk were comparable. However, Turkers tended to agree less with the pre-labeled mood clusters than MIREX evaluators. The system evaluation results generated using both sets of data were mostly the same except for detecting one statistically significant pair using Friedman's test. We conclude that MTurk can potentially serve as a viable alternative for ground truth collection, with some reservation with regards to particular mood clusters.
summary: In regards to data collection, we are in need of a large data set that captures human temporal intention. To collect this, prior and during the phases of experimental design, we examined several publications depicting crowd sourcing @cite_30 and most specifically Amazon's Mechanical Turk @cite_31 which has been used in generating ground truth data for a similar-scoped study in detecting music moods @cite_2 .