Detecting, Modeling, and Predicting User Temporal Intention

The content of social media has grown exponentially in the recent years and its role has evolved from narrating life events to actually shaping them. Unfortunately, content posted and shared in social networks is vulnerable and prone to loss or change, rendering the context associated with it (a tweet, post, status, or others) meaningless. There is an inherent value in maintaining the consistency of such social records as in some cases they take over the task of being the first draft of history as collections of these social posts narrate the pulse of the street during historic events, protest, riots, elections, war, disasters, and others as shown in this work. The user sharing the resource has an implicit temporal intent: either the state of the resource at the time of sharing, or the current state of the resource at the time of the reader \clicking . In this research, we propose a model to detect and predict the user\u27s temporal intention of the author upon sharing content in the social network and of the reader upon resolving this content. To build this model, we first examine the three aspects of the problem: the resource, time, and the user. For the resource we start by analyzing the content on the live web and its persistence. We noticed that a portion of the resources shared in social media disappear, and with further analysis we unraveled a relationship between this disappearance and time. We lose around 11% of the resources after one year of sharing and a steady 7% every following year. With this, we turn to the public archives and our analysis reveals that not all posted resources are archived and even they were an average 8% per year disappears from the archives and in some cases the archived content is heavily damaged. These observations prove that in regards to archives resources are not well-enough populated to consistently and reliably reconstruct the missing resource as it existed at the time of sharing. To analyze the concept of time we devised several experiments to estimate the creation date of the shared resources. We developed Carbon Date, a tool which successfully estimated the correct creation dates for 76% of the test sets. Since the resources\u27 creation we wanted to measure if and how they change with time. We conducted a longitudinal study on a data set of very recently-published tweet-resource pairs and recording observations hourly. We found that after just one hour, ~4% of the resources have changed by ≥30% while after a day the change rate slowed to be ~12% of the resources changed by ≥40%. In regards to the third and final component of the problem we conducted user behavioral analysis experiments and built a data set of 1,124 instances manually assigned by test subjects. Temporal intention proved to be a difficult concept for average users to understand. We developed our Temporal Intention Relevancy Model (TIRM) to transform the highly subjective temporal intention problem into the more easily understood idea of relevancy between a tweet and the resource it links to, and change of the resource through time. On our collected data set TIRM produced a significant 90.27% success rate. Furthermore, we extended TIRM and used it to build a time-based model to predict temporal intention change or steadiness at the time of posting with 77% accuracy. We built a service API around this model to provide predictions and a few prototypes. Future tools could implement TIRM to assist users in pushing copies of shared resources into public web archives to ensure the integrity of the historical record. Additional tools could be used to assist the mining of the existing social media corpus by derefrencing the intended version of the shared resource based on the intention strength and the time between the tweeting and mining

Reading the Correct History? Modeling Temporal Intention in Resource Sharing

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

Resurrecting My Revolution Using Social Link Neighborhood in Bringing Context to the Disappearing Web

Abstract. In previous work we reported that resources linked in tweets disappeared at the rate of 11 % in the first year followed by 7.3 % each year afterwards. We also found that in the first year 6.7%, and 14.6 % in each subsequent year, of the resources were archived in public web archives. In this paper we revisit the same dataset of tweets and find that our prior model still holds and the calculated error for estimating percentages missing was about 4%, but we found the rate of archiving produced a higher error of about 11.5%. We also discovered that resources have disappeared from the archives themselves (7.89%) as well as reappeared on the live web after being declared missing (6.54%). We have also tested the availability of the tweets themselves and found that 10.34 % have disappeared from the live web. To mitigate the loss of resources on the live web, we propose the use of a “tweet signature”. Using the Topsy API, we extract the top five most frequent terms from the union of all tweets about a resource, and use these five terms as a query to Google. We found that using tweet signatures results in discovering replacement resources with 70+ % textual similarity to the missing resource 41 % of the time