How to Predict Social Trends by Mining User Sentiments

Chepurna, Iuliia; Aghababaei, Somayyeh; Makrehchi, Masoud

doi:10.1007/978-3-319-16268-3_29

Cited by 3 publications

(5 citation statements)

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“…In the user-centric model, the content of a set of selected users is aggregated based on user timelines, to extract meaningful patterns [17], while in the content-based approach, the content of all individuals are combined together with respect to the event of interest [18]. Both of the aforementioned approaches are considered to be temporal models, which suffer from the challenge of retrieving tweets over time.…”

Section: Researchmentioning

confidence: 99%

“…The drawback behind the latter approach is that, the availability of user posts over time is not considered. In fact, there is no guarantee that sampled users are active on a daily basis, which is necessary for temporal models where content (content-based) [18] or user timelines (user-centric) are aggregated considering their timestamps [17]. The most common sampling approach is random sampling using streaming API, which allows retrieving 1% of real-time data with some specific parameters.…”

Section: Related Workmentioning

confidence: 99%

“…The task is crime trend (1) tweets_day = total_tweets/days prediction by leveraging Twitter data. Historical tweets have shown to be successful in predicting the directions of crime rates [17,18]. The problem of trend prediction is converted to a binary classification problem where the objective is to detect the directions of the targeted trends (in our case, crime trends).…”

Section: Temporal Classification Modelmentioning

confidence: 99%

“…In the second model, instead of data aggregation across all users, documents are generated from the individual opinions in different time slots [17]. If a user u 1 has a post at time t and user u 2 also posted something at the same time, the content of each is employed as a unique feature or an user-dependent feature rather than combining them…”

Section: User-centric Modelmentioning

confidence: 99%

“…However, there are many challenges in utilizing content of experts, whose corporate accounts in social media are normally managed by a group of employees, compared to random users. In many applications, ranging from contentbased [24] to user-centric [17], opinions of crowds collectively provide predictive signals for prediction models. In fact, by filtering experts we ignore the valuable content coming from crowd and we neglected the vast amount of information contributed by the citizens.…”

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confidence: 99%

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Activity-based Twitter sampling for content-based and user-centric prediction models

Aghababaei

Makrehchi

2017

Hum. Cent. Comput. Inf. Sci.

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BackgroundTwitter's public and open nature provides great opportunities for its users to actively participate in sharing their opinions and produce high quality content that is reflective of their tendencies and preferences in their day-to-day life [1]. This vast amount of publicly available user-generated content is applied to many applications ranging from tracking human social behavior [2][3][4], detecting events of interest [5][6][7], to smart business [8] where domain knowledge is collected through social media. These studies are either concerned with pulling Twitter and aggregating tweets as bulk or tracking historical tweets over time in order to find meaningful patterns for targeted events. The main challenge of the former studies is the limitation of the Twitter API in accessing only 1% of all existing tweets. However, despite this limitation, the latter studies are concerned with retrieving historical timelines of users.To tackle the above issues of retrieving more tweets beyond the 1% threshold and obtaining historical timelines, topic-based sampling and REST API are both shown to Abstract Increasingly more applications rely on crowd-sourced data from social media. Some of these applications are concerned with real-time data streams, while others are more focused on acquiring temporal footprints from historical data. Nevertheless, determining the subset of "credible" users is crucial. While the majority of sampling approaches focus on individual static networks, dynamic user activity over time is usually not considered, which may result in activity gaps in the collected data. Models based on noisy and missing data can significantly degrade in performance. In this study, we demonstrate how to sample Twitter users in order to produce more credible data for temporal prediction models. We present an activity-based sampling approach where users are selected based on their historical activities in Twitter. The predictability of the collected content from activity-based and random sampling is compared in a content-based and user-centric temporal model. The results indicate the importance of an activityoriented sampling method for the acquisition of more credible content for temporal models.Keywords: Twitter sampling, Temporal prediction models, Historical timelines, User activity, Activity-based sampling Open Access© The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Aghababaei and Makrehchi Hum. Cent. Comput. Inf. Sci. (2017) Page 2 of 20 Aghababaei and Makrehchi Hum. Cent. Comput. Inf. Sci. (2017) 7:3 be more effective [9,10]. In topic-based sampling [11], a set of specific keywords or hashtags are applied to collect tweets through the search API...

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Section: Researchmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Temporal Classification Modelmentioning

confidence: 99%

Section: User-centric Modelmentioning

confidence: 99%

mentioning

confidence: 99%

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