The ripple of fear, sympathy and solidarity during the Boston bombings

Lin, Yu‐Ru; Margolin, Drew

doi:10.1140/epjds/s13688-014-0031-z

Cited by 60 publications

(54 citation statements)

References 54 publications

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“…Similarly, over the past 5 years, social media has been increasingly used to understand the psychological and behavioral outcomes of individuals in a range of settings such as disasters (Lin & Margolin, ; Lin, Margolin, & Wen, ). This line of research examines the linguistic features of individuals’ social media posts to derive inferences about their mental health outcomes, the findings of which have been consistent with the studies of linguistic expressions among individuals with mental illness in clinical settings (Oxman, Rosenberg, & Tucker, ; Rude, Gortner, & Pennebaker, ).…”

Section: Social Network Neighborhood Disadvantages and Distressmentioning

confidence: 99%

Neighborhood disadvantage, residents' distress, and online social communication: Harnessing Twitter data to examine neighborhood effects

Booth

Lin

Wei

2018

Journal Community Psychology

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This study aims to examine the role of place‐based online social communications in buffering the relationship between neighborhood disadvantage and psychological distress using geo‐located Twitter feeds. We collected 1,981,802 geo‐tagged tweets posted within the physical boundaries of 78 neighborhoods located in Pittsburgh for 16 consecutive months, beginning July 15, 2013. Psychological distress was measured using sentiments expressed in tweets, and within‐ and between‐neighborhood online social communications were assessed using users’ Twitter activity. This study reveals differential effects of within‐ and between‐neighborhood online social communications. More online social communications within a neighborhood were associated with increased psychological sadness, anxiety, and distress in high‐poverty neighborhoods; a relationship that was not found in low‐poverty neighborhoods. This study suggests the buffering effects of online social communications may follow the same patterns as off‐line social support in disadvantaged neighborhoods. Considering the interactions between online spaces and neighborhoods may be important for planning and implementing neighborhood‐level interventions.

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Section: Social Network Neighborhood Disadvantages and Distressmentioning

confidence: 99%

Neighborhood disadvantage, residents' distress, and online social communication: Harnessing Twitter data to examine neighborhood effects

Booth

Lin

Wei

2018

Journal Community Psychology

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“…Error rate 40,41 determines that the predicted output values are inaccurate, ie, it is defined as a proportion of total number of bad predictions among all total inspected cases. Error rate 40,41 determines that the predicted output values are inaccurate, ie, it is defined as a proportion of total number of bad predictions among all total inspected cases.…”

Section: Performance Measuresmentioning

confidence: 99%

“…Error rate (ER) It is majorly measured on testing data sets. Error rate 40,41 determines that the predicted output values are inaccurate, ie, it is defined as a proportion of total number of bad predictions among all total inspected cases.…”

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confidence: 99%

Systematic literature review of sentiment analysis on Twitter using soft computing techniques

Kumar

Jaiswal

2019

Concurrency and Computation

123

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Sentiment detection and classification is the latest fad for social analytics on Web. With the array of practical applications in healthcare, finance, media, consumer markets, and government, distilling the voice of public to gain insight to target information and reviews is non-trivial. With a marked increase in the size, subjectivity, and diversity of social web-data, the vagueness, uncertainty and imprecision within the information has increased manifold. Soft computing techniques have been used to handle this fuzziness in practical applications. This work is a study to understand the feasibility, scope and relevance of this alliance of using Soft computing techniques for sentiment analysis on Twitter. We present a systematic literature review to collate, explore, understand and analyze the efforts and trends in a well-structured manner to identify research gaps defining the future prospects of this coupling. The contribution of this paper is significant because firstly the primary focus is to study and evaluate the use of soft computing techniques for sentiment analysis on Twitter and secondly as compared to the previous reviews we adopt a systematic approach to identify, gather empirical evidence, interpret results, critically analyze, and integrate the findings of all relevant high-quality studies to address specific research questions pertaining to the defined research domain. KEYWORDS machine learning, review, sentiment analysis, soft computing, Twitter INTRODUCTIONThe incessantly evolving dynamics of the Web in terms of the volume, velocity and variety of opinion-rich information accessible online, has made research in the domain of Sentiment Analysis (SA) a trend for many practical applications which facilitate decision support and deliver targeted information to domain analysts. Interestingly, the buzzing term ''big data'' which is estimated to be 90% unstructured 1 further makes it crucial to tap and analyze information using contemporary tools. Text mining models define the process to transform and substitute this unstructured data into a structured one for knowledge discovery. Use of classification algorithms to intelligently mine text has been studied extensively across literature. 2,3 SA, established as a typical text classification task, 4 is defined as the computational study of people's opinions, attitudes and emotions towards an entity. 5,6 It offers a technology-based solution to understand people's reactions, views and opinion polarities (positive, negative or neutral) in textual content available over social media sources.Research studies and practical applications in the field of SA have escalated in the past decade with the transformation and expansion of Web from passive provider of content to an active socially-aware distributor of collective intelligence. This new collaborative Web (called Web 2.0), 7 extended by Web-based technologies like comments, blogs and wikis, social media portals like Twitter or Facebook, that allow to build social networks based on professional relationship, i...

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“…While attempts have been to capture sentiment through tokenization [45], both sentiment and sympathy are hard to capture computationally. However, sympathy [35] and sentiment [15] can be detected from text. Therefore, we consider sympathy (is the tweet sympathetic or not towards the affected individuals?)…”

Section: Defining News Sympathy and Sentimentmentioning

confidence: 99%

Measuring, Understanding, and Classifying News Media Sympathy on Twitter after Crisis Events

Ali

Stratmann

Park

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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This paper investigates bias in coverage between Western and Arab media on Twitter after the November 2015 Beirut and Paris terror attacks. Using two Twitter datasets covering each attack, we investigate how Western and Arab media differed in coverage bias, sympathy bias, and resulting information propagation. We crowdsourced sympathy and sentiment labels for 2,390 tweets across four languages (English, Arabic, French, German), built a regression model to characterize sympathy, and thereafter trained a deep convolutional neural network to predict sympathy. Key findings show: (a) both events were disproportionately covered (b) Western media exhibited less sympathy, where each media coverage was more sympathetic towards the country affected in their respective region (c) Sympathy predictions supported ground truth analysis that Western media was less sympathetic than Arab media (d) Sympathetic tweets do not spread any further. We discuss our results in light of global news flow, Twitter affordances, and public perception impact.

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The ripple of fear, sympathy and solidarity during the Boston bombings

Cited by 60 publications

References 54 publications

Neighborhood disadvantage, residents' distress, and online social communication: Harnessing Twitter data to examine neighborhood effects

Neighborhood disadvantage, residents' distress, and online social communication: Harnessing Twitter data to examine neighborhood effects

Systematic literature review of sentiment analysis on Twitter using soft computing techniques

Measuring, Understanding, and Classifying News Media Sympathy on Twitter after Crisis Events

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