The Stability of Economic Correlations over Time

Pasek, Josh; Yan, Huirong; Conrad, Frederick G.; Newport, Frank; Marken, Stephanie

doi:10.1093/poq/nfy030

Cited by 15 publications

(7 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We further examine whether correlations between data streams are consistent across shorter time spans. In a prior study, we found that the correspondence between economic confidence and tweets about "jobs" was strong over an 8-year period but highly variable when the window was only a single year (Pasek et al, 2018). Replicating this analysis, we compared sentiment and approval over all possible yearlong windows in the current study.…”

Section: Robustness Of Correspondencesmentioning

confidence: 94%

“…Given that Twitter facilitates news dissemination and information diffusion, users might be tweeting about topics relevant to the entire public sphere (cf. Pasek et al, 2018). Similarly, the broadcasting nature of tweets could lead users to imagine a general audience for posts and consequently cater the content they produce (Marwick & Boyd, 2011;Schober et al, 2016).…”

Section: Tweets-as-societal-windowmentioning

confidence: 99%

“…There are many reasons to think that social media metrics may be sensitive to short-term variations different from the factors that influence survey measures (Pasek et al, 2018). In particular, posts may vary in magnitude and valence as a function of events that produce large public reactions (Guggenheim & Pasek, 2013;Jang & Pasek, 2015;Kwak, Lee, Park, & Moon, 2010;Zhao et al, 2011).…”

Section: Analytical Strategymentioning

confidence: 99%

“…More broadly, Twitter posts are designed for an audience rather than solicited in response to a formulated question (Marwick & Boyd, 2011;Schober et al, 2016). Although different from the data generating process underlying survey data, they could potentially tap areas of general societal attention (Jungherr, Schoen, Posegga, & Jürgens, 2016;Pasek, Yan, Conrad, Newport, & Marken, 2018). Thus, individually or collectively, they may capture some form of public opinion.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Who’s Tweeting About the President? What Big Survey Data Can Tell Us About Digital Traces?

Pasek

McClain

Newport³

et al. 2019

Social Science Computer Review

Self Cite

View full text Add to dashboard Cite

Researchers hoping to make inferences about social phenomena using social media data need to answer two critical questions: What is it that a given social media metric tells us? And who does it tell us about? Drawing from prior work on these questions, we examine whether Twitter sentiment about Barack Obama tells us about Americans’ attitudes toward the president, the attitudes of particular subsets of individuals, or something else entirely. Specifically, using large-scale survey data, this study assesses how patterns of approval among population subgroups compare to tweets about the president. The findings paint a complex picture of the utility of digital traces. Although attention to subgroups improves the extent to which survey and Twitter data can yield similar conclusions, the results also indicate that sentiment surrounding tweets about the president is no proxy for presidential approval. Instead, after adjusting for demographics, these two metrics tell similar macroscale, long-term stories about presidential approval but very different stories at a more granular level and over shorter time periods.

show abstract

Section: Robustness Of Correspondencesmentioning

confidence: 94%

Section: Tweets-as-societal-windowmentioning

confidence: 99%

Section: Analytical Strategymentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Who’s Tweeting About the President? What Big Survey Data Can Tell Us About Digital Traces?

Pasek

McClain

Newport³

et al. 2019

Social Science Computer Review

Self Cite

View full text Add to dashboard Cite

show abstract

“…Sentiment analysis is a broad class of methods used to detect opinions or mood from text. Although there are a range of approaches used in context-specific situations to detect positive and negative arXiv:1802.07859v2 [cs.SI] 15 May 2019 opinions about a topic [8,9,10,11,12], here we restrict the definition to include the general sentiment analysis methods used to detect mood. Sentiment analysis has also been used for applications in public health to evaluate reactions and attitudes to certain current events [13], health interventions such as vaccination [14], human mobility [15], and outcomes such as seasonal affective disorder and obesity [16,17,18].…”

Section: Introductionmentioning

confidence: 99%

Modeling Spatiotemporal Factors Associated With Sentiment on Twitter: Synthesis and Suggestions for Improving the Identification of Localized Deviations

Shah¹,

Martin²,

Coiera³

et al. 2019

J Med Internet Res

View full text Add to dashboard Cite

Background Studies examining how sentiment on social media varies depending on timing and location appear to produce inconsistent results, making it hard to design systems that use sentiment to detect localized events for public health applications. Objective The aim of this study was to measure how common timing and location confounders explain variation in sentiment on Twitter. Methods Using a dataset of 16.54 million English-language tweets from 100 cities posted between July 13 and November 30, 2017, we estimated the positive and negative sentiment for each of the cities using a dictionary-based sentiment analysis and constructed models to explain the differences in sentiment using time of day, day of week, weather, city, and interaction type (conversations or broadcasting) as factors and found that all factors were independently associated with sentiment. Results In the full multivariable model of positive (Pearson r in test data 0.236; 95% CI 0.231-0.241) and negative (Pearson r in test data 0.306; 95% CI 0.301-0.310) sentiment, the city and time of day explained more of the variance than weather and day of week. Models that account for these confounders produce a different distribution and ranking of important events compared with models that do not account for these confounders. Conclusions In public health applications that aim to detect localized events by aggregating sentiment across populations of Twitter users, it is worthwhile accounting for baseline differences before looking for unexpected changes.

show abstract