social media data
Recently Published Documents





2022 ◽  
Vol 22 (1) ◽  
pp. 1-25
Florian Meier ◽  
Alexander Bazo ◽  
David Elsweiler

A fundamental tenet of democracy is that political parties present policy alternatives, such that the public can participate in the decision-making process. Parties, however, strategically control public discussion by emphasising topics that they believe will highlight their strengths in voters’ minds. Political strategy has been studied for decades, mostly by manually annotating and analysing party statements, press coverage, or TV ads. Here we build on recent work in the areas of computational social science and eDemocracy, which studied these concepts computationally with social media. We operationalize issue engagement and related political science theories to measure and quantify politicians’ communication behavior using more than 366k Tweets posted by over 1,000 prominent German politicians in the 2017 election year. To this end, we first identify issues in posted Tweets by utilising a hashtag-based approach well known in the literature. This method allows several prominent issues featuring in the political debate on Twitter that year to be identified. We show that different political parties engage to a larger or lesser extent with these issues. The findings reveal differing social media strategies by parties located at different sides of the political left-right scale, in terms of which issues they engage with, how confrontational they are and how their strategies evolve in the lead-up to the election. Whereas previous work has analysed the general public’s use of Twitter or politicians’ communication in terms of cross-party polarisation, this is the first study of political science theories, relating to issue engagement, using politicians’ social media data.

2022 ◽  
Vol 53 ◽  
pp. 101391
Oleksandr Karasov ◽  
Stien Heremans ◽  
Mart Külvik ◽  
Artem Domnich ◽  
Iuliia Burdun ◽  

2022 ◽  
pp. 216770262110626
Tal Yatziv ◽  
Almog Simchon ◽  
Nicholas Manco ◽  
Michael Gilead ◽  
Helena J. V. Rutherford

The COVID-19 pandemic has been a demanding caregiving context for parents, particularly during lockdowns. In this study, we examined parental mentalization, parents’ proclivity to consider their own and their child’s mental states, during the pandemic, as manifested in mental-state language (MSL) on parenting social media. Parenting-related posts on Reddit from two time periods in the pandemic in 2020, March to April (lockdown) and July to August (postlockdown), were compared with time-matched control periods in 2019. MSL and self–other references were measured using text-analysis methods. Parental mentalization content decreased during the pandemic: Posts referred less to mental activities and to other people during the COVID-19 pandemic and showed decreased affective MSL, cognitive MSL, and self-references specifically during lockdown. Father-specific subreddits exhibited strongest declines in mentalization content, whereas mother-specific subreddits exhibited smaller changes. Implications on understanding associations between caregiving contexts and parental mentalization, gender differences, and the value of using social-media data to study parenting and mentalizing are discussed.

Massimo Regona ◽  
Tan Yigitcanlar ◽  
Bo Xia ◽  
Rita Yi Man Li

Artificial intelligence (AI) is a powerful technology that can be utilized throughout a construction project lifecycle. Transition to incorporate AI technologies in the construction industry has been delayed due to the lack of know-how and research. There is also a knowledge gap regarding how the public perceives AI technologies, their areas of application, prospects, and constraints in the construction industry. This study aims to explore AI technology adoption prospects and constraints in the Australian construction industry by analyzing social media data. This study adopted social media analytics, along with sentiment and content analyses of Twitter messages (n = 7906), as the methodological approach. The results revealed that: (a) robotics, internet-of-things, and machine learning are the most popular AI technologies in Australia; (b) Australian public sentiments toward AI are mostly positive, whilst some negative perceptions exist; (c) there are distinctive views on the opportunities and constraints of AI among the Australian states/territories; (d) timesaving, innovation, and digitalization are the most common AI prospects; and (e) project risk, security of data, and lack of capabilities are the most common AI constraints. This study is the first to explore AI technology adoption prospects and constraints in the Australian construction industry by analyzing social media data. The findings inform the construction industry on public perceptions and prospects and constraints of AI adoption. In addition, it advocates the search for finding the most efficient means to utilize AI technologies. The study helps public perceptions and prospects and constraints of AI adoption to be factored in construction industry technology adoption.

2022 ◽  
Vol 12 (1) ◽  
Koustuv Saha ◽  
Asra Yousuf ◽  
Ryan L. Boyd ◽  
James W. Pennebaker ◽  
Munmun De Choudhury

AbstractThe mental health of college students is a growing concern, and gauging the mental health needs of college students is difficult to assess in real-time and in scale. To address this gap, researchers and practitioners have encouraged the use of passive technologies. Social media is one such "passive sensor" that has shown potential as a viable "passive sensor" of mental health. However, the construct validity and in-practice reliability of computational assessments of mental health constructs with social media data remain largely unexplored. Towards this goal, we study how assessing the mental health of college students using social media data correspond with ground-truth data of on-campus mental health consultations. For a large U.S. public university, we obtained ground-truth data of on-campus mental health consultations between 2011–2016, and collected 66,000 posts from the university’s Reddit community. We adopted machine learning and natural language methodologies to measure symptomatic mental health expressions of depression, anxiety, stress, suicidal ideation, and psychosis on the social media data. Seasonal auto-regressive integrated moving average (SARIMA) models of forecasting on-campus mental health consultations showed that incorporating social media data led to predictions with r = 0.86 and SMAPE = 13.30, outperforming models without social media data by 41%. Our language analyses revealed that social media discussions during high mental health consultations months consisted of discussions on academics and career, whereas months of low mental health consultations saliently show expressions of positive affect, collective identity, and socialization. This study reveals that social media data can improve our understanding of college students’ mental health, particularly their mental health treatment needs.

Yong Gao ◽  
Yuanyuan Chen ◽  
Lan Mu ◽  
Shize Gong ◽  
Pengcheng Zhang ◽  

2022 ◽  
pp. 188-205
Erkan Çiçek ◽  
Uğur Gündüz

Social media has been in our lives so much lately that it is an undeniable fact that global pandemics, which constitute an important part of our lives, are also affected by these networks and that they exist in these networks and share the users. The purpose of making this hashtag analysis is to reveal the difference in discourse and language while analyzing Twitter data and to evaluate the effects of a global pandemic crisis on language, message, and crisis management with social media data. This form of analysis is typically completed through amassing textual content data then investigating the “sentiment” conveyed. Within the scope of the study, 11,300 Twitter messages posted with the #stayhome hashtag between 30 May 2020 and 6 June 2020 were examined. The impact and reliability of social media in disaster management could be questioned by carrying out a content analysis based totally on the semantic analysis of the messages given on the Twitter posts with the phrases and frequencies used.

2022 ◽  
Vol 18 (1) ◽  
pp. 0-0

Social media data become an integral part in the business data and should be integrated into the decisional process for better decision making based on information which reflects better the true situation of business in any field. However, social media data are unstructured and generated in very high frequency which exceeds the capacity of the data warehouse. In this work, we propose to extend the data warehousing process with a staging area which heart is a large scale system implementing an information extraction process using Storm and Hadoop frameworks to better manage their volume and frequency. Concerning structured information extraction, mainly events, we combine a set of techniques from NLP, linguistic rules and machine learning to succeed the task. Finally, we propose the adequate data warehouse conceptual model for events modeling and integration with enterprise data warehouse using an intermediate table called Bridge table. For application and experiments, we focus on drug abuse events extraction from Twitter data and their modeling into the Event Data Warehouse.

Sign in / Sign up

Export Citation Format

Share Document