Using Social Media to Measure Labor Market Flows
Dolan Antenuccia
Michael Cafarellab
Margaret C. Levensteinc
Christopher Réd
Matthew D. Shapiroe
University of Michigan

March 2014
Revised May 2, 2014

This project is part of the University of Michigan node of the NSF-Census Research Network(NCRN) and is supported by the National Science Foundation under Grant No. SES 1131500.
We are grateful to Tomaz Cajner, Michael Elsby, Jeremy Nalewaik, Linda Tesar, Kenneth West,
Justin Wolfers, and seminar participants at the NBER Summer Institute, Vanderbilt University,
the University of Wisconsin, the University of Michigan, and the Federal Reserve Board for
helpful comments and to Mark Fontana for excellent research assistance.

aDepartment of Electrical Engineering and Computer Science, University of Michigan
Department of Electrical Engineering and Computer Science, University of MichigancSurvey Research Center and Ross School of Business, University of MichigandDepartment of Computer Science, Stanford UniversityeDepartment of Economics and Survey Research Center, University of Michigan and NBERb

Using Social Media to Measure Labor Market Flows
ABSTRACTSocial media enable promising new approaches to measuring economic activity and analyzing
economic behavior at high frequency and in real time using information independent fromstandard survey and administrative sources. This paper uses data from Twitter to create indexesof job loss, job search, and job posting. Signals are derived by counting job-related phrases in
Tweets such as “lost my job.” The social media indexes are constructed from the principal
components of these signals. The University of Michigan Social Media Job Loss Index tracks
initial claims for unemployment insurance at medium and high frequencies and predicts 15 to 20
percent of the variance of the prediction error of the consensus forecast for initial claims. The
social media index is shown to have a greater signal to noise ratio than initial claims in providing
information about the true state of job loss in the economy and is, therefore, of value to policy
makers in need of high frequency, real-time indicators. The social media indexes provide realtime indicators of events such as Hurricane Sandy and the 2013 government shutdown.
Comparing the job loss index with the search and posting indexes indicates that the Beveridge
Curve has been shifting inward since 2011.
The social media job loss prediction is posted on the web each week at
http://econprediction.eecs.umich.edu/.
JEL: C81, C82, E24, J60
Dolan Antenucci
Department of Computer Science and Engineering
University of Michigan
Ann Arbor, MI 48109-2121
dol@umich.edu

Michael Cafarella
Department of Computer Science and Engineering
University of Michigan
Ann Arbor, MI 48109-2121
michjc@umich.edu

Margaret C. Levenstein
Ross School of Business and Survey Research Center
University of Michigan
Ann Arbor, MI 48106-1248
maggiel@umich.edu

Christopher Ré
Department of Computer Science
Stanford University
353 Serra Mall
Stanford, CA 94305-9025
chrismre@cs.stanford.edu

Matthew D. Shapiro
Department of Economics and Survey Research Center
University of Michigan
Ann Arbor, MI 48109-1248
and NBER
shapiro@umich.edu

This paper develops new measures of flows in the labor market using social media data.
Specifically, we use Twitter data to produce and analyze new weekly estimates of job flows from
July 2011 to early November 2013. We present methods for validating such novel economic
measures and articulate principles for assessing the usefulness of time series derived from social
media (Section I). We do this first by comparing our estimates with official data. Our Twitterderived job loss index tracks initial claims for unemployment insurance (UI) and carries
incremental information relative to both lagged UI data and the consensus forecast (Section II).
We also propose social media indexes to measure concepts with weaker analogues in official
statistics—job search and job posting—and then use these measures to study shifts in therelationship between posting and job loss (Section III). Finally, we show that our high-frequency
social media index tracks events that affect the job market in real time, such as Hurricane Sandy
and the federal government shutdown (Section IV). We show that the index has greater signal tonoise ratio than the official initial claims series in providing information about the true state ofjob loss in the economy. It is, therefore, of potential value to policy makers in need of such
indicators, particularly in formulating policies when there is uncertainty about whether the
economy is at a turning point.
Social media provide an enormous amount of information that can be tapped to create
measures that potentially serve as both substitutes and complements to traditional sources of datafrom surveys and administrative records. The use of social media to construct economic
indicators has a number of potential benefits. First, social media data are available in real timeand at very high frequency. Such timely and high-frequency data may be useful to policymakers
and market participants who often need to make decisions prior to the availability of official
indicators. The fine time-series resolution may be particularly helpful in identifying turning2

points in economic activity. Second, social media data are potentially a low-cost source of
valuable information, in contrast to traditional surveys that are costly for both the respondent andthe organization collecting the data. Third, social media offer a distinctive window into
economic activity. They represent naturally-occurring personal communication amongindividuals about events in their everyday lives without reference to any particular economic
concept. Like administrative data, but unlike surveys, social media challenge economists to map
the observed information into the economic concept being measured. Fourth, social media can beused to answer questions we would have liked to ask in surveys had we known about events inadvance. In ordinary survey design, we frame the questions and then collect the data. Social
media allows us to reverse this order and generate ex post “surveys.” For example, we use theindexes to examine the impact of two shocks to the labor market, Hurricane Sandy in October
2012 and the October 2013 government shutdown.
This paper implements social media indexes for job flows. Why do we focus on job
flows? Substantively, job flows are of central interest to economists, market participants, andpolicymakers. Practically, the weekly frequency of the official UI claims data makes them agood benchmark for testing the performance of our social media measures. We have Twitter datafor only 28 months, so there is insufficient time-series variation against which to compare
national aggregates such as GDP or employment. Given that the UI series is available at highfrequency and without sampling error, one might ask what the Twitter signal has to add. We
chose the unemployment flows concept as the case study for this paper precisely because of theavailability of a high quality, frequent series against which to compare it. This comparison
should give researchers confidence to use the techniques developed in this research to study
domains that are not as well-covered by official statistics.

3

Official UI data and our job loss index track related but not identical phenomena. Our
aim is therefore to track the official index with our social media index, but not perfectly so. Since
they are designed to measure the same general economic concept, they should certainly havestrong co-movements. Yet they should not be perfectly correlated because of differences in
population, timing, and the underlying data generation process. Indeed, one of the promises ofsocial media for measuring economic concepts is that it will provide incremental information
relative to official statistics. We find that the social media index not only does a very good jobof tracking the official data, it also has important independent movements that we show—both
statistically and anecdotally—carry incremental information.
I. Twitter Data
Twitter is a social media service through which individuals and enterprises can post short, 140character messages of any subject of their choosing. These messages are known as Tweets.1
Unless restricted by the user, they are available publicly. These messages can be read on the
web through internet browsers and by a variety of other software. Individuals can subscribe to
the Tweets of particular users, or subscribe to them by topic (denoted by a hash tag, i.e., akeyword with a “#” prefix). A common use of Twitter is to communicate news about life events
to a community of friends. These can be mundane (“I am standing at 3rd and Elm waiting for abus”), communicate plans or whereabouts (“Let’s meet at Showcase Cinema at 7:15 to see the
new Bond film”), or momentous (“George and I are pleased to announce the birth of Polly, 7lb,8oz”). The following Tweet contains a job loss phrase of the type we analyze.

1

The length of a Tweet derives from the 160 character limit on an SMS text message. Twitter
reserves 20 characters for the identifier.
4

2011 was interesting. I ended an engagement, got laid off, started asmall biz, and it looks like I’ll be moving this year too. Whew!

Our analysis is based on a roughly 10 percent sample of all Tweets between July 2011 and earlyNovember 2013. The dataset contains 19.3 billion Tweets and is 43.8 terabytes (TB) in size.
Web search queries, an alternative source of naturally-occurring web data, have also been
examined for their economic content. Web search queries are framed very differently fromsocial media data and contain different types of information. Our approach based on social
media thus is both similar and complementary to approaches based on web searches (Choi and
Varian 2009a, b, Scott and Varian 2013). There are several differences in technique between oursystem and that of Choi and Varian. First, web search queries and social media data likely
capture different kinds of information. Social media data capture communications amongindividuals about their lives, while web search queries reflect individuals trying to findinformation on the web. These datasets capture phenomena that likely overlap, but are not
identical. For example, an individual may be likelier to announce a new job to friends via social
media, but may be more willing to reveal personal health information via a web search query.
Second, Twitter messages are tied to a user, who exists in a public social network. User metadata can be used, for example, to classify messages by demographic groups or geography.
Potentially, user information could be used to relate Twitter messages across users. As of thiswriting, Google Trends does not give information about the person who generated the search
query, so controlling for individual characteristics or following a user over time is not possible.
(The current version of Google Trends does give country-level geographic distributions of theusers who generated the search queries.) Last, but not least, raw Google search queries are not5

public and so cannot be analyzed directly; in contrast, Tweets are public. Google’s web search
query data are made public only via the Google Trends tool. It currently does not reveal actual
frequencies for search terms, but instead places frequencies on a 0-100 scale, making some usesof the data difficult or impossible. The techniques used to collect and prepare the data are notpublic. In contrast, the methods we propose here are transparent, so researchers can more easily
inspect and reproduce analyses performed on the resulting signal data.
Use of social media to construct time-series indicators has recently come under criticism
along several dimensions (see Lazer et al., 2014). These criticisms include lack of transparencyof the statistical procedures, lack of replicability because of the use of proprietary data, andinstability of time series. Specifically, the well-known Google Trends index for the flu suffersfrom these problems. As we will discuss, our Twitter based approach was designed not to havethese issues. We explicitly describe the statistical procedure. We post the signals used toconstruct the index on the Web. Our index is the result of a collaboration between economists
and computer scientists focused on the underlying economic phenomena. Because we selectsignals from Twitter completely independently from series we are trying to track, our procedure
is immune from the overfitting problem raised by Lazer et al.
A. Strategies for Converting Social Media into Data
A core challenge in this work is to develop a rigorous methodology to convert the corpus ofsocial media texts into time-varying signals that have both predictive and explanatory power forlabor market flows. We can convert a given set of relevant Tweets into a signal by first counting
their frequency in each 24-hour period in the sample period and then compiling these daily
counts into a single time-varying signal. Obtaining m signals amounts to choosing m relevant

6sets of Tweets. Clearly, the power of our social media index depends on how well we choosethese sets.
Given the very large number of Tweets, automated statistical techniques for choosing
predictive features—in our case, sets of Tweets—are appealing (Guyon and Elisseeff, 2003).
These techniques, however, pose serious challenges for our task. First and most basic, thetechnique for enumerating all the possible signals in the Tweet collection—i.e., modeling thefeature space—is not obvious. One approach is to create a Tweet set for each unique Twitter
author; another is to compose a Tweet set for every k-gram, in which a k-gram is a sequence of kor fewer consecutive ordered words found in the Tweet corpus. Considering a restrictive set offeatures may make feature selection easier, while a larger set of features may enable creation of
an index with better predictive power. Second, even a relatively restrictive set of potential
features, such as k-grams with k≤4, yields vastly more features than we have time-series
macroeconomic data.2 (There are roughly one billion 4-grams that occur at least 20 times in ourdata.) Some features with high correlations will in fact be entirely spurious and thus carry no
predictive power. Other features may be predictive but not causal (e.g., Lysol as a feature forflu). Such features can be useful for the predictive model but may be logically opaque to humanobservers (that is, the Tweets in a set will not have an obvious common thread or will have acommon thread that appears to be nonsensical).
The computer science members of the research team are exploring the problem of feature
selection when applied to social media and any macroeconomic or similar topic.

2

In their development of a flu index using Google web search queries, Ginsberg, et al. (2009)
followed a variable ranking strategy that chose signals that were highly correlated with a target
signal. In preliminary work, we considered the correlation of phrases found in Tweets withweekly unemployment. None of the top 100 most correlated phrases had any plausible
connection to unemployment (Antenucci et al. 2013a).
7

Macroeconomic tasks tend to offer very small in-sample datasets in comparison to other dataintensive trained system tasks in computer science. For example, web search engines can exploit
billions of human judgments about web page relevance, derived by observing users’ clicks on
search engine result pages. In the absence of large datasets that can automatically validatefeature selections, the techniques under development would have the researcher describe feature
preferences (that is, provide “domain knowledge”) and then observe a set of features suggestedby the system. The researcher would then reject features that violate real but implicit researcher
preferences. (We give several examples of this procedure from our own experience in thesection below.) One early version of this automated system offered suggestions based on a
combination of user-suggested terms, thesauri, and statistics derived from web text (Antenucci etal. 2013a,b). Ideally, this system would give results in interactive timeframes, but the massive
number of possible feature combinations (roughly 2.7 x 10103 when choosing 10 features fromamong 4-grams in our corpus) makes known suggestion techniques infeasible. Solving these
problems is the subject of ongoing research, and could substantially lower the researcher burdens
associated with applying social media techniques to any novel topic.
In this paper, we solve this problem by first limiting our analysis to k-grams, which areessentially repeated cross sections of Tweets, aggregated first to days and then weeks. For the
economics task at hand, we chose signals from a feature space in which each feature corresponds
to a k-gram where k≤4. Second, we narrow the feature space further by using domain knowledge
to select signals that we strongly believe are causally connected to job loss. Specifically, theresearch team identified terms that it believes are indicative of the phenomenon being measured,
based on knowledge and expertise in the area. We describe this procedure in more detail in thenext section.

8

This approach has drawbacks. First, we may unwittingly add bias during our selection ofphrases. Second, some Tweet sets cannot be described at all (e.g., because we restrict ourselves
to 4-grams, we cannot characterize the set of all Tweets that contain the five-word phrase, “myMom no longer works”). Finally, the feature space does not automatically group phrases that are
textually distinct but semantically similar (e.g., “I got fired” and “my boss canned me” express
the same idea but are not identical k-grams). Our choice of feature modeling has the benefits ofbeing easy to describe and enabling many Tweet sets that are understandable to the user (e.g., all
Tweets that contain the phrase, “I lost my job”). Moreover, despite its restrictiveness, our design
is sufficient to demonstrate that social media data contain genuinely useful information aboutlabor flows.
B. Implementation
To implement the domain knowledge strategy, the research team developed a list of phrases
related to job loss and unemployment that it expected to be found in Tweets that carried
information about job loss in general and initial claims for unemployment insurance in particular.
The phrases we use to aggregate signals of job loss and unemployment are listed in Table 1. A
space is denoted as “|” and a wildcard as “*” as in the detailed descriptions in Appendix Table 1.
The process for generating the list of phrases includes the following steps:
A priori specification of terms such as “lost job,” “laid off,” and “unemployment”
that we expect to be contained in Tweets of interest.



Expansion of the specification of the target phrases to include plausible misspellings
and wildcards to capture variants such as “lost my job” or “lost his job.”Deletion of phrases where—upon inspection—it becomes clear that the a priori
specified phrases have little to do with the labor market.

9The first column of Table 1 gives the ten job loss and unemployment signals that we will
analyze. We allow for variants in spelling and spacing. The variants we consider include the 27
search phrases listed in Appendix Table 1.
There are some terms one might expect to include a priori, but which we exclude or
include only in combination with other words. For example, we do not include a search for thewords “fired,” “benefits,” and “insurance” alone because each was used much more frequently inunrelated contexts (e.g., fired up). Note that “unemployment benefits” or “unemployment
insurance” are captured because we do include any k-gram including the word “unemployment.”
In general, singleton terms can be problematic. We originally included the term “sacked” buteliminated the signal from further analysis because its frequency in the data—several orders ofmagnitude greater than other employment-related terms—suggested that its use referred to otherlinguistic meanings. Similarly, we eliminated “let go” because it appeared much more frequently
than other employment-related phrases and seemed to have other plausible meanings.
In the case of the phrase “lost * work,” inspection of the matched k-grams clearly
indicated nearly universal non-employment related concepts. Many phrases referred to computer
problems such as “lost all my work” and “lost my #$% work,” as well as happier references suchas “lost in my work” and “lost Beethoven work.” As a consequence, we excluded all candidates
related to this signal in the creation of the job loss measure. Having the wildcard in this search
was critical for revealing that the “lost work” phrases were not about employment.
One concern about the use of social media to measure economic activity is that it willcapture comments on releases of official statistics rather than provide independent measures of10

activity.3 We did not see evidence that there is a lot of Tweeting about the Department ofLabor’s release of initial claims data, but the monthly Employment Situation release does get a
lot of attention and might account for a significant number of mentions of “unemployment.”Indeed, the Bureau of Labor Statistics (BLS) plans to use Twitter as an official release channel,
so re-Tweets of the unemployment report may be a significant confound in the future. To checkfor the importance of the unemployment report per se on Tweets about unemployment, weestimate a linear regression with the unemployment signal as the dependent variable and adummy for weeks containing the unemployment report as the regressor. The estimatedrelationship is
r_unempt = 49.4 + 17.4 emp_sitt + ut
(1.9) (4.1)
where r_unemp is the unemployment signal, emp_sit is the Employment Situation dummy, and uis the residual.4 Tweets about unemployment are about a third higher in an Employment
Situation week than average, so we purge Tweet-derived signals containing “unemployment” ofthe Employment Situation effect using a regression as shown above.
Table 1 presents summary statistics of the signals. The signals are expressed as weekly
rates per million Tweets. While the signals derived from the selected phrases are fairly rare—
between 0.5 and 54 per million Tweets—there are so many Tweets that the signals still provide arich dataset. Of the 19.3 billion Tweets reflected in Table 1, there are 2.4 million associated withjob loss and unemployment. The signals have roughly comparable coefficients of variation, so
there is potentially information in each of them. The correlation matrix in Table 2 shows that the3

There is evidence that a substantial amount of communication over social media consists oflinks to internet sites of content creators (CNN, Justin Bieber’s Tweets, etc.). See Goel, Watts,
and Goldstein (2012).
4Standard errors are in parentheses.
11signals from the selected phrases are positively correlated (with the exception of “pink slip”), sothey do appear to be picking up related phenomena in the Tweets.
In order to preserve degrees of freedom while extracting as much information as possible
from the Twitter signals, we perform a principal components analysis on the ten signals. Table 3
reports the factor loadings and variances. Not surprisingly, given the positive and fairly uniform
correlation structure reported in Table 2, the first factor has fairly uniform coefficients across thesignals and accounts for 43 percent of the variance. The next four factors each account for about
10 percent of the variance. Figure 1 plots the factors estimated over the entire sample from July
2011 through early November 2013. The first panel shows only the first factor, and the second
panel shows the first four. The first factor is fairly volatile in the second half of 2011 and has a
noticeable downward trend into 2012, when it flattens. This pattern is interrupted in late 2012.
In 2013, the downward trend evident throughout the period resumes. The next panel adds thefactors 2, 3, and 4. By construction, they have signal less and have spikes that might be suspect.
Factor 2 has a spike in late 2011 that matches a spike in Factor 1, so it might be genuinely related
to job flows.
On the other hand, note that Factor 3 is dominated by the “pink slip” signal (see Table 3).
There is evidently a spike in that signal in December 2012. Without it, Factor 3 would not haveemerged from the principal components analysis as having significant variance, so unless we areprepared to believe this spike is job related, it should be discounted.5

5

We suspect that the spike was driven by Tweets about the November 19, 2012 launch of amarketing campaign titled “Pink Slip” featuring football player Tom Brady. See Business Wire,
November 19, 2012 “UGG for Men Launches New ‘Pink Slip’ Integrated Campaign for Holiday
2012 Featuring Tom Brady.”12

C. Relating Social Media Data to the Economy and Economic Data
These signals from social media and the factors that summarize them are new measures of
economic activity. They are not based in any way on standard measures using conventionalsources of data. It is natural to ask how they relate to a standard measure of economic activity:
initial claims for unemployment insurance (UI). The initial claims data are well-suited forevaluating the social media signals. First, they are available at weekly frequency. Given that wehave just over two years of Twitter data, a high-frequency economic indicator for comparison isvery important. Second, initial claims for UI are a direct measure of transitions in the labormarket. Hence, they are likely to have much more high-frequency variation than variables thatmeasure stocks (e.g., the unemployment rate). We expect that social media data will be usefulprecisely for measuring such high-frequency changes in activity.
Figure 2 shows initial claims for UI (left scale) and the first factor from the Twitter job
loss and unemployment signals (right scale). The social media series is estimated completely
independently from the new claims data. The relationship between these two indicators of job
loss is quite strong—both in the general trend and in some notable spikes. Over the sample
period from July 2011 to early November 2013, initial claims have a general downward trend in
new claims. They flatten in the first half of 2012 and then resume the downward trend in 2013.
The social media series has a very similar pattern.
There are also some high-frequency changes in new claims—notably the spike in late fall2012. Our indicator also captures that spike in job loss. We will investigate this spike,associated with Hurricane Sandy, in some detail below.
Note also that the fit of the social media series to the new claims series is not perfect.
Aside from period-by-period variation, the social media series has a spike in 2011 that is not in13the initial claims data. More interestingly, it does not indicate the slowdown in job loss seen in
the new claims data in September 2013. The social media information contains independent
information about the job market. Indeed, as we will discuss below, the drop in initial claims in
September relates to a processing problem in California.
Not all job loss is associated with applications for UI, so we are not seeking simply to
predict UI. Nonetheless, the high- and low-frequency association of the series with the official
data is reassuring.
We can test the association of the social media signal with the UI initial claims data
statistically. Table 4 presents regressions of initial claims on the social media series and forcomparison, lagged initial claims and the consensus forecast.6,7 The social media series is not as
good a predictor of new claims as are the lagged dependent variable or the consensus, though ofcourse there is no reason to expect or hope it to be. Nonetheless, it is strongly predictive of newclaims and remains significant in the regressions that include the lagged dependent variable andthe consensus.
II. A Real-time Predictor from Social Media Data
The social media series for job loss successfully tracks official data at both high and low
frequency. This section constructs a real-time index for predicting initial claims for
unemployment insurance, and evaluates its ability to provide a real-time indicator of economic
activity. In contrast with the previous section, which sought to estimate time series from social

6

The consensus forecast is produced by Bloomberg Surveys (various dates). It is the median
forecast of a panel of approximately 50 economists.
7
For this set of regressions, the social media index is normalized to make the regression
coefficients comparable, normalizing so it has the same mean and standard deviation as thedependent variable. This normalization, of course, has no effect on the t-statistics or fit of theregression.
14

media and show that they are related to economic activity, this section aims to construct a
predictor that is feasible in real-time.
A. Constructing the Real-time Predictor
To construct the University of Michigan Social Media Job Loss Index, we estimate a model
relating initial claims for unemployment insurance to social media signals recursively, using only
data that are available at the point of the prediction. The Twitter data are available almost
immediately, so we can construct a prediction of the current week’s new claims with virtually nolag. The procedure is as follows:
1. Estimate the factors on the social media signals from the beginning of the sample throughthe current week.
2. Estimate the University of Michigan Social Media Job Loss Index by regressing real-time
initial claims data on the factors. The regression coefficients are updated each week.
3. Construct the prediction as the fitted value for the current week from that regression.
4. Update the data weekly and repeat this procedure.
Precise details of the procedure are provided in the appendix.
We carry out this procedure recursively over periods ending July 7, 2012 throughNovember 2, 2013. The starting period of the estimation is always July 16, 2011.8 We consider
various specifications for the regression in step 2. Table 5 reports the estimates of these
specifications for the final period. Table 6 reports the root mean squared error of these different
specifications using the predictions estimated recursively. The specification with a constant and8

We experimented with various alternatives to having a fixed starting period. These included
estimating over a rolling, one-year window and using the whole period, but with exponentiallydeclining weights on older observations. The results were quite similar, so we report the simpler
specification using OLS estimated over all the data available in real time.
15the first factor yields a strongly significant coefficient and an adjusted R2 of 59 percent.9 Adding
factors 2 through 4 adds little to the fit of the regression. Table 6 shows that the RMSE of thespecification with one factor is the lowest, so that is our preferred specification based both on
goodness of fit and parsimony.
Table 5 also includes specifications with two additional explanatory variables.
Specification 5 includes the seasonal factor for initial claims as an additional explanatory
variable to evaluate whether there is discernible seasonality in the relationship between the social
media index and initial claims. Flows into unemployment are highly seasonal with peaks in
December/January and the summer. The Twitter data may also exhibit seasonality, but with lessthan three years of data, we cannot seasonally adjust it. Using the new claims seasonal factor
implicitly seasonally adjusts, assuming the same seasonality in both series. The seasonal factor
is small and insignificant, so we do not include it in our preferred specification. Given that job
loss is indeed seasonal, it is interesting to note that the social media mentions of job loss do nothave the same spike as the official data. An interpretation of this finding is that a predictable job
transition relating, for example, to the end of a seasonal spell of employment, is not somethingthat one would mention in a Tweet using the phrases we use to construct the signals. The
absence of such predictable transitions is not necessarily a problem for the social media index—
indeed for some purposes it might be an advantage—but it needs to be kept in mind for the use
and interpretation of the indicator.
The last column of Table 5 considers whether the announcement of the Bureau of Labor
Statistics unemployment data affects the index. As we describe in Section I, the signals
mentioning “unemployment” are already purged of this announcement effect. The estimate in
9

Note that the estimate in the first column of Table 5 is the same, apart from normalization, ofthat in the third column of Table 4B.
16

column (7) checks whether this processing is sufficient for removing the announcement effectfrom the index. The dummy for weeks that the unemployment rate is released is insignificant, so
the procedure discussed in Section I does appear to suffice.
B. Analyzing the Real-Time Social Media Job Loss Index
Figure 3 shows our preferred specification for the social media index with a constant and Factor
1. It is plotted against the initial claims data. The shaded area is the first year of data. Because it
is not feasible to estimate the factors and perform the regression recursively, as described in steps
1 and 2 above, they are estimated over the whole period. The balance of the data shown in
Figure 3 is estimated recursively, as described in the previous section. The social media index
tracks the official data closely, both in overall trend and in some of the movements. On the other
hand, it carries independent information about job loss, for example, indicating a spike in 2011not present in the official data and failing to show the decline in job loss in September 2013 at
the end of our sample. This drop in reported initial claims in the official data in September 2013
relates to a data processing issue in California;10 this is an example of where the social media
index does not suffer from measurement error encountered by the official data, and thus may
more closely track the true state of the economy.
Additionally, the social media index tracks increases in job loss evidently associated withthe government shutdown during the first two weeks of October 2013. The index rises
noticeably in the first half of October and declines by about the same amount in the second half
10

Employment and Training Administration’s Unemployment Insurance Weekly Claims Report,
issued September 19, 2013, reports a decrease of 25,412 UI claims in California “due to Labor
Day holiday and computer system updates.”
http://www.dol.gov/opa/media/press/eta/ui/eta20131889.htm The following week the
Employment and Training Administration’s Unemployment Insurance Weekly Claims Report,
issued September 26, 2013, reported that a comparable increase in UI claims in California“reflects return to 5 day workweek and a full week of processing after computer system
updates.” See http://www.dol.gov/opa/media/press/eta/ui/eta20131953.htm
17

of the month. Initial claims have a similar pattern (after accounting for the rebound from theresolution of the processing issues in California).11
While the UI series and social media index generally move together, they are certainly
not perfectly correlated. This is to be expected, since they measure different things. While partof the proof of concept is to show that the social media index moves with the official data, theaim is not to replicate the official data perfectly. For myriad reasons relating to the concept
being measured, the coverage and take-up of unemployment insurance benefits, and the makeupof the samples, the social media index measures something different from the official series.
Nonetheless, our findings that they are related do provide evidence that the social media index isa meaningful measure of economic activity.
C. Assessing the Information in the Real-Time Social Media Job Loss Index
Next we ask whether, from the perspective of predicting the state of the economy in real-time,
there is incremental information in our social media index. The results in the previous section
suggest this might be so. We know from column 6 of Table 4 that the consensus forecast is avery good predictor of the initial claims data, but that the social media factor has incremental
explanatory power. In order to address the question of incremental information, we compare ourSocial Media Job Loss Index to the consensus forecast on the eve of the initial claims
announcement. This consensus forecast is based on a survey of market experts several days prior
to the release of initial claims for UI. Table 7 reports the results of this analysis. First, we
examine the preliminary report of new UI claims. We subtract the consensus estimate from thepreliminary UI claims report to calculate the error in the consensus view. We then compare these11

Federal workers apply to a different unemployment insurance system. They are not included in
the preliminary initial claims data used to construct the real-time job loss index.
18

errors to the Social Media Job Loss Index, which we construct based on information available inreal time as described above.
To assess the incremental information in the Social Media Job Loss Index, we examine
the regression of the error (preliminary initial claims minus consensus) on the Social Media Job
Loss Index minus the consensus (Table 7, Column 1). The social media index carries
incremental information. It is statistically significant and explains about 15 percent of thevariation in the surprise relative to the consensus. In Table 7, Column 2 we report an estimatethat separates the impact of the University of Michigan Social Media Job Loss Index and theconsensus. The University of Michigan Social Media Job Loss Index remains a significant
predictor of the error in the consensus, while the coefficient on consensus itself is roughly equal
and in opposite sign to the coefficient on the University of Michigan Social Media Job Loss
Index minus consensus in the first estimate (the p-value of the test of equal and opposite
coefficients is 0.16). These results are included to show that the correlation of the consensus
with the surprise is not driving the result. Finally, in Column 3 we include a lagged index. It
has a very small coefficient that is not significantly different from zero, suggesting that, after one
week the information content in the Tweets had been incorporated into the consensus view.
Indeed, there is little evidence of any lags in the relationship between the social media signals
and the UI data.
In the second part of Table 7, we compare our social media index to the UI claims,
revised one week after the initial numbers. The results are similar to those for the preliminary UI
claims, except that the explanatory power of the social media index increases to 19 percent of thevariance, suggesting that the social media index is better at predicting the true, revised UI
number than it is at predicting the original estimate. This finding suggests that the social media

19index is capturing information about the true state of the job market that is not captured in either
the consensus or the preliminary UI claims estimate. The incremental information in the social
media index is relevant relative to both the preliminary and revised data. Policymakers andforecasters will be more interested in information about the revised data. Market participants
may be more interested in the incremental information for the preliminary announcement.
Figure 4 shows the incremental information in the social media index on a week-by-weekbasis. The figures show the surprise (initial claims minus consensus) and the part predicted by
the University of Michigan Social Media Job Loss Index (that is, fitted value of the regression of
the surprise on the University of Michigan Social Media Job Loss Index minus consensus) forthe preliminary and revised initial claims data. Again, we do not aim to track all the surpriseand indeed account for 15 to 20 percent of it. Much of the surprise is serially uncorrelated noisewith no intrinsic interest. The social media index does capture some of the unexpected increase
in initial claims in early 2012 and some of the swing from positive to negative surprises in thelast quarter of 2012 to the first quarter of 2013.12
D. Providing a Real-Time Economic Indicator from Social MediaThis research project has implemented the creation of the University of Michigan Social Media
Job Loss Index in real time. At the end of each week ending Saturday, our automated computer
program processes the latest Tweets, recalculates the job loss index based on the one-factor
model described in the previous section, and updates the prediction. We post the prediction on
the web each week. We also post the underlying daily signals used to construct the index.13 In

12Choi and Varian (2009b) use Google search queries to predict initial claims. For “Welfare andUnemployment” (though less so for “Jobs”) Google Trends captures the increase in
unemployment at the onset of the Great Recession.
13
See http://econprediction.eecs.umich.edu/ for the predictions andhttp://ringtail.eecs.umich.edu/unempSignalsDailyUpdate.csv for the daily signals. Making the20this way, we are able to provide policymakers, forecasters, and other interested parties with auseful high-frequency economic indicator with virtually no lag between availability of the source
data and availability of the indicator. Such virtually contemporaneous information should be
useful to policymakers and market participants who need to make decisions in real time withincomplete information.
E. Demographics
A concern about the use of social media data is that those who participate in social media are notrepresentative of the population.14 We can assess this concern by estimating demographic
characteristics of Tweeters. For a subset of signals, we can probabilistically estimate the age andsex of the sender based on attributes of the Tweet. By examining the distribution of word
choices in a set of Tweets written by people with known age and gender, we can train statisticalmodels to predict age and gender for the author of a novel Tweet. The training set for the age
predictor includes up to 3,200 Tweets for each of 24,000 users, while the training set for genderincludes 12,500 users’ Tweets. We identify users with known age and gender by searching for
Tweets that contain self-admission of demographic details, for example, “I'm 30 years old now,
but still live with my mom” or “I’m a strong woman.” The statistical technique we employ is arandomized decision tree classifier (Breiman 2001).
We use six age brackets: 14-18, 19-21, 22-24, 25-34, 35-44, and 45-64. Classifieraccuracy on held-out data is 47.3 percent for age, and 82.4 percent for gender. Appendix Table 3

inputs into our index available addresses concerns about transparency and replicability ofindexes from social media raised by Lazer et al. (2014). These inputs are raw counts of Tweets
as described in the text. We cannot redistribute individual Tweets.
14
See, for example, Mislove et al. (2011). Note that the authors use a sample of Twitter data that
ends in 2009, arguably well before Twitter became a mass phenomenon. Consequently, though
their method is of interest, their demographic findings represent early adopters who are likely
less representative than the current population of Twitter users.
21

shows the fraction of job-related signals by age and sex for the subset of signals for which we
can estimate demographic characteristics.15 Though the distribution of age and sex does notmatch the population, the use of social media to communicate about job-related issues is much
more evenly spread across demographic groups than one might have expected. In particular,
middle-aged and older individuals are over-represented in the job-related signals, in comparison
to how frequently they Tweet overall. Note that senders of a signal need not be Tweeting about
themselves: for example, messages by a teenager could be commenting on a job transition for aparent. Even so, it is reassuring that a substantial majority of our job-related signals are from theworking-age population.
III. Job Search and Job Posting
The previous section focuses on job loss—a concept that has a good high-frequency analogue in
official data. In this section we broaden the range of labor market flows to include job searchand posting. The first section constructs indexes and the following section uses them to study
shifts in the Beveridge curve over the recovery from the Great Recession.
A. Indexes
We create and describe two additional series related to search, matching, and labor market
equilibrium. Specifically, we examine Tweets containing phrases indicating that the Tweeter issearching for a job (e.g., “find,” “look,” “need,” “search,” or “seek,” each followed by “job” or“work”) and others that suggest that the Tweeter is searching for an employee (“hiring,” and“job” or “work” followed by “opportunity” or a phrase indicating location or job type). The

15

The estimates are based on data only through June 2013. After that time, Twitter changed its
public API, thereby reducing our ability to gather large numbers of Tweets for specificindividuals. Further work is required to quantify how this change in data availability will affectthe accuracy of demographic classification, and if accuracy is reduced, what novel methods canbe used to improve quality.
22

signals for job search and job posting are listed in Table 8 and the detailed phrases are given in
Appendix Table 2.
Signals reflecting a job posting are much more frequent than those reflecting job search.
Search signals are comparable in their frequency to those reflecting job loss (compare to Table1). Table 9 presents the correlation matrix of all the job search and job posting terms. While“find” is not closely correlated with any other terms, the other search terms are positively andsimilarly correlated. As expected, the posting terms are more closely correlated to one anotherthan to the search terms. The “seek” term is correlated across search and posting terms, and issyntactically related to both, so it is included in both sets of terms.
There are analogues to the Twitter signals for search and posting in conventional data
sources:

The unemployment rate is a measure of search activity, especially since the BLS requiresa modicum of job search activity as part of the CPS definition of being unemployed.

Help wanted advertising has been a traditional source of data on vacancies.16



The BLS JOLTS data provide a survey measure of job openings.17

For the job loss index developed in the previous section, UI claims were a particularly goodanalogue in official data. New claims for UI are high frequency. Moreover, both the “lost job”
Tweets and the new claims data are direct measures of flows. In contrast, the match of the
16

The Conference Board formerly produced a monthly Help Wanted Advertising Index based on
print advertisements, but discontinued it in 2008. It currently produces a comparable series,
drawn from internet postings of job advertisements, the Conference Board Help Wanted OnLine
data series http://www.conference-board.org/data/helpwantedonline.cfm
17
In the JOLTS, an opening needs to meet three criteria: A specific position exists; work could
start within 30 days; and the firm is actively seeking workers from outside its location to fill the
position. See Bureau of Labor Statistics, “Job Openings and Labor Turnover Report” (2013).
The JOLTS data also have data on separations that can be compared to our job loss index. The
social media index is more frequent and more timely than the JOLTS data. The JOLTS data areproduced monthly and are available about two months after the reference week of the survey.
23

search and posting terms with the unemployment rate and vacancies is not as clear-cut. First, it
is not obvious which side of the market is generating the search and posting terms. Second,
unlike in the job loss analysis, there may be a mismatch between stocks and flows whencomparing the social media signals to unemployment or vacancies. There has been imaginative
recent work on addressing the measurement issues relating to stocks versus flows in the labormarket (see Davis, Faberman, and Haltiwanger (2013) and Barnichon et al. (2012)). Given theless than perfect analogy between our search and matching measures and potential official
benchmarks, we do not pursue an econometric analysis along the lines of the previous section.
We do, however, discuss our series in the context of recent findings from the JOLTS.
To construct the search and posting indexes, we do a factor analysis as discussed in theprevious section. Table 10 presents the factor loadings for the first factor for the search andposting signals. As expected from the correlation matrix, the “find” signal has a smaller loading.
Aside from the “find” signal in the first set of loadings and the “hiring” signal in the second, theloadings are fairly even across the signals. Figure 5 shows the indexes based on the first factors
reported in Table 10. Since we are not benchmarking against an official index, there is no renormalization of this index. Hence, the index is not measured in meaningful units: it is thechange in the index that has meaning. We use the same recursive procedure as before: after thestarting period shaded in gray, the indexes are estimated recursively.
Overall, there is a downward trend similar to that found in the job loss index. There arealso spikes at various points, for example, December 2012, but not the previous December. The
dip in October 2013 associated with the government shutdown is discussed in Section D.
Note that the downward trend in our posting and search indexes is not seen in the JOLTS
job openings rate over the same period. The JOLTS job opening rate was 2.4 percent in July24

2011 (the start of our sample). In 2012 and 2013, it was higher than in 2011, but with nodiscernible trend. It reached 2.8 percent in early 2012 and—with small ups and downs—wasstill at 2.8 in late 2013 (see Bureau of Labor Statistics 2014, Chart 1). In contrast, our search andposting indexes have a substantial movement down in the second half of 2011 and a slightdownward trend in 2012 and 2013. These differences suggest that the social media indexes aremeasuring something different from the JOLTS openings, perhaps because of stock/flow
considerations.18
Demographics of search and posting. Note that the demographics of search and posting
Tweets are quite different. The modal group sending the job search Tweets are teenagers withthe balance of search Tweets evenly spread across age groups. On the other hand, the majorityof the job posting Tweets is from the 45 and older age group. This age pattern provides external
support for the validity of our coding of the search and posting signals in that one would expect
those posting jobs to be substantially older.
B. Beveridge Curves
We use our labor market indexes to study the relationship between job loss, search, and posting
akin to the Beveridge Curve. Figure 6A shows the Search/Job Loss Beveridge Curve and Figure
6B show the Posting/Job Loss Beveridge Curve. Of course, these indicators are not identical tovacancies and unemployment in the standard Beveridge Curve, but they have potential to shedlight on labor market equilibrium. Note that the relationship between the variables is mainly

18

The Conference Board HWOL series has a similar flattening in 2013 after a recovery from the2009 trough. The JOLTS separation rate (see Chart 2 of BLS (2014)) have a similar pattern tothe job openings rate: moving slightly up from 3.2 percent to 3.4 percent from mid-2011 to thebeginning of 2012, then bouncing between 3.1 and 3.4 in 2012 and 2013 with no discernible
trend. Note that we are comparing our series to JOLTS for the period where we have data,
beginning in 2011. The JOLTS openings rate has a strong upward movement from its trough in2009 at the depth of the Great Recession.
25

positive, especially in the posting/job loss figure that is most analogous to the traditional curve.
Taken at face value, this finding suggests that over this period (July 2011 to early November2013), inward shifts of the Beveridge Curve dominated movement along the Beveridge Curve.
This finding is consistent with recent work by Barnichon, et al. (2012) and Hobijn and Şahin
(2013) that shows that there were significant outward shifts of the Beveridge Curve, as measured
by JOLTS data, with the onset of the Great Recession.19 There are various explanations of theoutward shift in the Beveridge Curve that started at the onset of the Great Recession relating todeterioration in matching jobs to the unemployed, especially those unemployed for long
durations. Our social media indexes suggest a reversal of this deterioration of labor market
conditions at least since mid-2011.20,21

19

In Section A, we noted that the JOLTS openings and separation rates both shift up from 2011
to 2012 and then exhibit no trend. In contrast, as discussed in the previous sub-section, our job
loss and search and posting indexes both have opposite shifts over the same period. The work
cited concerning the JOLTS focuses on the bigger shifts in the Beveridge curve surrounding the2009 trough. Unfortunately, our Twitter data does not encompass the Great Recession.
20
In a related application, Baker and Fradkin (2013) use Google Trends search data to construct
an index measuring searching for a job. They link this index at the MSA level to administrative
data on unemployment insurance claimants in Texas. They find that job-search related use of
Google search increases across time and localities as the fraction of claimants approach theexpiration of their benefits. Garthwaite, Gross, and Notowidigdo (2013) use Google Trends for“job openings” to show that search activity increased when beneficiaries lost their eligibility for
TennCare (Medicaid) coverage.
21
An obvious application of these data is to test for changes in search behavior surrounding the
expiration of unemployment benefits. We have investigated this possibility. There is only aslight and not statistically significant increase in our social media search index after the
expiration of extended benefits in late 2013. Note however that the expiration of extended
benefits—though a large subject for political debate and news coverage—is not discernible in theofficial national, continuing claims data. Indeed, the official index of continuing claims
increases after the termination of long-term benefits. (There is sharp January seasonality in thisseries that makes analysis of policy changes around the first of the year quite difficult.) The
aggregate impact of the policy is small relative to other effects on labor market flows, such as
seasonality, so it is not surprising that we cannot find effects in the social media index.
26

IV. Using Social Media Indicators as High-Frequency Indicators
In this section we assess the usefulness of the social media indexes as economic indicators forbusiness conditions and policy analysis. The social media indexes provide contemporaneous
indications of the effects of shocks that potentially disrupt the labor market. Using a signal
extraction technique, we show that the social media index of job loss has superior measurement
properties relative to new claims.
A. Labor Market and Hurricane Sandy
One of the potential benefits of analyses using social media data is that the researcher may
examine the impact of unexpected events as they happen without relying on recall or chancesurveying during such events.22 We examine signals related to Hurricane Sandy to demonstrate
this type of analysis. Figure 7 shows all Tweets (measured in thousands) that include the words“Sandy” or “hurricane.” Unlike our previous analysis, carried out at weekly frequency, Figure 7
shows daily data. Additionally, we simply present raw counts rather than rates or a statisticalindex, because we have no historical baseline.23 Not surprisingly, the number of such Tweets
increases sharply as Hurricane Sandy headed toward the northeast coast of the United States inlate October 2012. The series peaks on October 29 when Hurricane Sandy hit New York and
New Jersey. Figure 7 also shows the subsets of signals that include either our job loss or searchand posting terms. (Note that the scale differs by a factor of 1,000 from that of the total.) We cansee that search and posting terms spike simultaneously with Sandy’s arrival, while job loss

22

For example, Kimball et al. (2006) had a survey in the field when Hurricane Katrina hit; theyused it to study the hurricane’s effect on psychometric measures of happiness.
23
We do see increased mentions of hurricanes in the signals during the storm seasons in our data.
27

references increase just after the hurricane arrived. The job market related signals continued atelevated rates well after mentions of the storm per se peaked.24
B. Government Shutdown
The effects of the government shutdown in October 2013 are clearly evident in the job loss index(Figure 3) and in the search and posting indexes (Figure 5). All have pronounced falls in the firsttwo weeks during the shutdown and equal bounce backs in the weeks following the shutdown.
These results imply that the shutdown had a significant effect on labor market activity, but thatthe effect was short lived.
Interestingly, beginning in September 2013, there are changes in search and posting
relative to job loss that are consistent with movements along the Beveridge Curve (Figure 6).
These observations are dominated by the effects of the government shutdown and reopening thatare evident in the time series. Hence the labor market disruption associated with the government
shutdown appears to be a classic demand shock instead of a disruption of the matching function.
This episode illustrates the usefulness of social media for measuring and analyzing theimpact of unexpected events. Our social media indexes provide high-frequency andcontemporaneous information that is not available in conventional sources.
C. Monitoring the Economy: What Series Should the Fed Follow?
The Federal Reserve is called on to take policy actions based on imperfect and lagging indicatorsof the economy. There are relatively few high-frequency indicators of real economic activity.
Social media indexes may prove to be an important addition to its data arsenal especially during
period of economic volatility or at turning points. In her first press conference, Federal Reserve
24

There is a sharp peak on December 7, 2012 in Tweets that mention “hurricane” or “Sandy”
and “unemployment,” presumably reflecting the release of the first Bureau of Labor Statistics
Employment Situation report to reflect unemployment data after Sandy. This example illustrates
the importance of controlling for data releases when analyzing the social media signals.
28

Chairman Janet Yellen stressed the importance of monitoring labor market data beyond theunemployment rate.25 Though initial claims is the earliest available indicator of labor market
activity, there is reluctance to give it much credence because of its volatility. In this section we
use a very simple statistical model to suggest that the social media index carries more signal
about the true state of job flows.
To assess the signal in the social media index we estimate a very simple statistical model.
Suppose that the job loss xt* is unobserved. Suppose further that we have two indicators of job
loss, initial claims denoted y1t and the social media index denoted y2t . Suppose further that thetrue state of job loss follows a random walk with innovation t and that the two indictors arenoisy measures of the true state with serial and mutually uncorrelated errors  1t and  2t . Hence,
suppressing constants,
xt*   t
y1t   t   1t  1t 1y2t   t   2t  2t 1

describes the joint behavior of the observable indicators. Table 11 shows the estimated standard
deviations of the disturbances,   ,   , and   .26 Recall that, except for scaling, the two
1

2

25

“Now, the Committee has never felt that the unemployment rate is a sufficient statistic for thelabor market. I think if I had to choose one indicator of the labor market, the unemployment rate
is probably as good a one as I could find. But in assessing the real state of slack in the labormarket and ultimately of inflationary pressures that might—or deflationary pressures that could
result from that—it’s appropriate to look at many more things. And that’s why the Committee
now states we will look at a broad range of information. So the closer we get as we narrow in on
coming closer to the target we want to achieve, we will be carefully considering many indicatorsof how close are we to our targets.” (Federal Reserve Board 2014). While Yellen is clearlyreferring to official indicators such as labor market participation and flows from the JOLTS, we
expect that the Fed would be interested in social media indicators provided they can be shown tocarry information.
26
Estimation is via method of moments on demeaned data. The sample period is July 16, 2011
through November 2, 2013. Note that the identification of the parameters derives both from the29

indicators are totally independent of each other. Given the short sample available, even this very
parsimonious model is not that well pinned down by the data. As a result, the variance of theunobserved true state of the economy is not that precisely estimated. The specification of themodel in differences rather than levels also, of course, reduces the fit. The estimated standard
deviation of the social media indicator is much smaller than that of initial claims, suggesting thatthe social media index provides a more precise estimate of the true state of the underlying job
loss process.
The model formalizes what is apparent in the plots of the series: The initial claims data
are much more volatile, so they have a much lower signal-to-noise ratio than the social media
index. Figure 8 makes this concrete by showing the initial claims and social media indicatorsplotted against the implied estimate of the underlying unobserved state of job loss using theparameters in Table 11. The unobserved state is estimated using the Kalman filter. The shaded
area (after November 2, 2013) is entirely out of sample, in that the parameters in Table 11 thatgenerate this series are estimated on data prior to November 2.
Figure 8 shows graphically what is implied by the estimates reported in Table 11: thesocial media index is a better indicator of the underlying state of the job market than is initialclaims. Note that during the “out of sample” period, initial claims has been very volatile,reflecting processing problems, weather fluctuations, and possibly changes in program benefits.
The initial claims series also appeared to be falling, suggesting improvements in the labormarket, that are not present or as strong in the social media index. In the latest data, the initialclaims index has increased, reinforcing the view from the social media index that job loss had
cross correlation of the indicators and the serial correlation of each of them. That is, given thestochastic model, the univariate representation of each indicator is MA(1) in differences.
Consistent with the specified model, the indicators do indeed have negative MA(1) coefficients
univariately.
30

not declined. Had the Fed been monitoring the social media index during this period, the index
would have reinforced its decision to maintain monetary support in a way that the initial claims
series did not.
V. Conclusion
This paper addresses the challenge of turning the vast output of social media into data that can beused to create meaningful measurements. Doing so requires processing a very large dataset,coding social media signals for analysis, and using statistical methods to transform them into
economic data. This paper accomplishes these tasks. It creates a social media signal of job loss
that closely tracks initial claims for unemployment insurance. Despite obvious differences in theunderlying processes generating unemployment insurance claims and Tweets about job loss, thesocial media index tracks the official data at both high and medium frequencies. We construct areal-time index and show that this index has information about initial claims not reflected ineither the consensus forecast or the lagged data. The indexes shed light on specific events suchas Hurricane Sandy and the government shutdown.
Based on an admittedly short sample period, a simple statistical analysis suggests that thesocial media index is a better indicator of the true state of job loss than is initial claims. There isno other official statistic that is available at as high frequency and in close to real time forassessing job flows. Given the volatility of the official series, including in early 2014 when theFederal Reserve was beginning to contemplate a change in policy direction, having additional
informative real-time, high-frequency measures of job loss should be valuable to policy makers.
We began our analysis with a concept—job loss—that has a relatively well-measured
analogue in high-frequency official data. Having shown that a social media index can track aconcept that is relatively well-measured, we turn to concepts that are less well measured. In

31particular, we construct indexes of job search and job posting, concepts of keen interest to
analysts of the labor market, but less well measured in official statistics. We apply these series
to show that the Beveridge Curve appears to be shifting inward since mid-2011—reversing
outward shifts that other researchers identified during the Great Recession.
Longer time series and further analysis are needed to confirm the usefulness of social
media in constructing indicators of economic activity. Nonetheless, this paper has demonstrated
that it is both feasible and useful to infer information about the state of the labor market frompostings on social media that are generated by individuals in the normal course of their social
interactions. Variables derived from social media can be both substitutes and complements todata generated from surveys and administrative records by statistical agencies and the private
sector. They have the promise of providing measurements at relatively low cost, with highfrequency, and virtually in real time, so they have potential advantages over traditional data
sources. That is not to suggest, however, that social media could supplant official statistics.
Official statistics provide necessary benchmarks for understanding even the best measured
variables from social media. In practice, the rapid evolution of the use of social media could
make the relationship between the measurement and the underlying fundamental being measured
unstable. Our recursive procedure in constructing the University of Michigan Social Media Job
Loss Index is one approach to addressing this potential instability. As we accumulate longer
time series, research using methods described in this paper is necessary to evaluate the extent towhich social media data do track activity. Nonetheless, as with the search and posting series
constructed in this paper, social media data provide an opportunity to track hard-to-measurecomponents of economic activity by capturing information that has been previously neglected oris difficult to measure in traditional sources.

32

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34

Table 1. Summary Statistics for Signals: Job Loss and Unemployment(Weekly Rate per Million Tweets)
Standard
Coefficient of
SignalMean
Deviation
Variation
Axed
3.25
1.51
0.46
Canned
8.86
3.42
0.39
Downsized
0.49
0.25
0.51
Outsourced
2.11
1.35
0.64
Pink slip
1.34
1.31
0.98
Lost job
3.21
0.86
0.27
Fired job
27.45
6.67
0.24
Been fired
15.19
6.76
0.45
Laid off
15.70
3.59
0.23
Unemployment
53.33
20.07
0.38
Note: Sample period is July 16, 2011 through November 2, 2013 (weeks ending Saturday). Sample is19.3 billion total Tweets of which 2.4 million are job loss and unemployment related.. See Appendix
Table 1 for detailed descriptions of phrases for signals.

Table 2. Correlation of Job Loss and Unemployment Signals
Axed Canned Downsized Outsourced Pink slip Lost job Fired job Been fired Laid off
Unemployment
Axed
1
Canned0.37
1
Downsized
0.34
0.29
1
Outsourced
0.18
0.31
0.34
1
Pink slip
-0.05
0.00
-0.10
-0.12
1
Lost job
0.45
0.49
0.46
0.40
0.02
1
Fired job
0.48
0.46
0.28
0.18
-0.08
0.52
1
Been fired
0.45
0.36
0.04
0.01
-0.03
0.30
0.65
1
Laid off
0.43
0.52
0.46
0.43
-0.05
0.59
0.63
0.24
1
Unemployment
0.35
0.40
0.50
0.44
-0.14
0.54
0.47
0.21
0.66
1
Note: Sample period is July 16, 2011 through November 2, 2013. The Unemployment signal is purged of the Employment Situation effect.
Table 3. Factor Loadings on Job Loss and Unemployment Signals
Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8 Factor 9 Factor 10
Signal:
Axed
0.65
0.29
-0.06
0.39
0.45
0.13
-0.33
0.07
0.03
-0.04
Canned
0.68
0.11
0.14
-0.36
0.21
-0.56
-0.02
0.11
0.04
-0.05
Downsized
0.60
-0.42
0.04
0.51
0.01
-0.18
0.36
0.14
-0.12
0.01
Outsourced
0.52
-0.54
0.02
-0.40
0.33
0.37
0.13
0.09
-0.09
-0.03
Pink slip
-0.11
0.22
0.95
0.04
-0.03
0.13
0.03
0.11
0.01
-0.02
Lost job
0.78
-0.07
0.19
0.03
0.04
0.00
0.08
-0.56
0.12
0.02
Fired job
0.77
0.41
-0.11
-0.05
-0.27
0.11
0.05
-0.02
-0.24
-0.28
Been fired
0.51
0.72
-0.17
-0.08
0.04
0.14
0.32
0.11
0.11
0.20
Laid off
0.83
-0.13
0.08
-0.07
-0.26
0.00
-0.29
0.03
-0.26
0.25
Unemployment
0.76
-0.29
-0.04
0.01
-0.32
0.09
-0.10
0.18
0.42
-0.06
Variance of factor
4.27
1.40
1.02
0.72
0.60
0.55
0.46
0.42
0.36
0.19
Cumulative
fraction of variance
0.43
0.57
0.67
0.74
0.80
0.86
0.90
0.95
0.98
1.00
Note: Sample period is July 16, 2011 through November 2, 2013 (weeks ending Saturday). Principal component factors calculated based on thecorrelation matrix of signals shown in Table 2.

Table 4. Predicting Initial Claims: Consensus, Social Media Factor, and Lagged Dependent Variable
A. Preliminary Data
Constant
Lagged initial claims

(1)
90.24
(21.98)0.75(0.06)

Consensus forecast

(2)
30.16
(23.09)0.92(0.06)

Social media: factor 1 (scaled)
Adjusted R2

(3)
98.46
(22.93)

0.57

0.64

0.73
(0.06)
0.53

(1)
74.73
(20.27)0.80(0.05)(2)
46.33(20.75)(3)
103.43(20.02)(4)
31.57(23.56)0.05(0.16)
0.86(0.18)

0.63

(5)
43.00
(21.51)0.48(0.07)

0.40(0.07)
0.65

(6)
19.87
(22.48)0.67(0.10)
0.27(0.08)
0.66

(7)
23.60
(22.72)0.17(0.15)
0.48(0.21)
0.29(0.09)
0.66

B. Revised Data
Constant
Lagged initial claims
Consensus forecast
Social media: factor 1 (scaled)

0.88(0.06)(4)
46.56(20.55)0.27(0.15)
0.61(0.16)(5)
43.36(18.81)0.50(0.07)(6)
34.17(19.56)0.59(0.08)
0.32(0.07)
0.71

(7)
34.22
(19.28)0.29(0.14)
0.30(0.16)
0.33(0.07)
0.72

0.73
0.38
(0.05)(0.07)
2
Adjusted R
0.64
0.67
0.59
0.67
0.71
Note: Sample period is July 16, 2011 through November 2, 2013. Standard errors in parentheses.
Dependent variable: Initial Claims for Unemployment Insurance (preliminary data in panel A, revised data in panel B).
Regressors: Lagged dependent variable, consensus forecast, and social media factor 1 scaled to have same units as initial claims.

Table 5. Constructing the Social Media Job Loss Index(1)
Constant
368.26(1.52)
Factor 1
4.70(0.36)
Factor 2
Factor 3
Factor 4
Seasonal factor for initial claims
Employment Situation week

(2)

(3)

(4)

(5)

(6)

368.25(1.50)
4.70(0.35)-2.35(1.07)

368.23(1.50)
4.71(0.35)-2.35(1.07)-1.52(1.48)

368.23(1.48)
4.71(0.35)-2.35(1.06)-1.52(1.47)
3.76(2.05)

365.77(9.11)
4.69(0.36)

368.81(1.73)
4.69(0.36)

2.54(9.16)-2.43(3.66)

Adjusted R2
0.59
0.60
0.60
0.61
0.59
0.59
Note: The dependent variable is the Department of Labor Initial Claims for Unemployment Insurance (thousands, seasonally adjusted). The
independent variables are the job loss and unemployment factors. The Social Media Job Loss Index is based on regressions re-estimated eachweek using real-time data available as of the prediction period, as described in text. This table presents the estimates for the final week in thesample. Sample period is July 16, 2011 through November 2, 2013. Standard errors in parentheses.

Table 6. Prediction Errors of Social Media Job Loss Index
Root Mean Squared Error
Preliminary Data
Revised Data

Specification(1) Factor 1
21.9
19.2
(2) Factor 1,2
22.7
20.0
(3) Factor 1,2,3
23.7
21.4
(4) Factor 1,2,3,4
22.6
20.6
(5) Factor 1, Seasonal Factor
22.1
19.4
(6) Factor 1, Employment Situation Week
22.1
19.3
Note: Table gives the root mean squared error (RMSE) of the Social Media Job Loss Index for initial claims for unemployment insurance(preliminary data and revised data). The models and RMSEs are estimated recursively, using data from July 16, 2011 forward, for weeks ending
July 16, 2011 through November 2, 2013.

Table 7. Incremental Information in Social Media Job Loss Index
(1)
(2)
(3)
(4)
(5)
(6)
Dependent Variable
Preliminary Initial Claims
Revised Initial Claims− Consensus− Consensus
Constant-9.41-55.49-10.57-6.78-75.54-7.29(3.16)(53.78)(3.43)(2.92)(49.19)(3.18)
Social Media Job Loss Index – consensus
0.72
0.63
0.75
0.71
(0.20)(0.22)(0.18)(0.21)
Social Media Job Loss Index
0.85
0.94
(0.25)(0.23)
Consensus
-0.72
-0.75
(0.20)(0.18)
Lag of0.19
0.09
Social Media Job Loss Index – consensus(0.22)(0.20)
Adjusted R2
0.15
0.15
0.15
0.19
0.20
0.18
Note: Sample period is July 16, 2011 through November 2, 2013 (recursive sample). Standard errors in parentheses.
Dependent variables: Columns (1)-(3), Preliminary initial claims minus consensus; Columns (4)-(6), Revised initial claims minus consensus.

Table 8. Summary Statistics for Signals: Job Search and Job Posting(Weekly Rate per Million Tweets)
Standard
Coefficient of
Category
SignalMean
Deviation
Variation
25.23
4.96
0.20
Search
Find
25.31
8.28
0.33
Look
90.74
19.79
0.22
Need
2.13
0.93
0.44
Search
0.64
0.29
0.45
Search and postingSeek
220.81212.790.96
Posting
Hiring161.14
59.97
0.37
Job
384.37
67.66
0.18
Work
Note: Sample period is July 16, 2011 through November 2, 2013 (weeks ending Saturday). Sample is17.2 billion Tweets (116 weeks, 148.21 million Tweets per week on average). See Appendix Table 2 fordetailed descriptions of phrases for signals.

Table 9. Correlation of Job Search and Job Posting Signals
Find
Look
Need Search
Seek Hiring
1
Find
0.20
1
Look
0.34
0.85
1
Need
0.14
0.45
0.45
1
Search
0.25
0.33
0.29
0.45
1
Seek
-0.01
0.05
0.21
0.13
0.21
1
Hiring
0.26
0.24
0.13
0.30
0.46
0.24
Job
0.39
0.50
0.52
0.31
0.50
0.19
Work
Note: Sample period is July 16, 2011 through November 2, 2013.

Job

Work

1
0.56

1

Table 10. Job Search and Job Posting Factors: Loadings of First Factor, Alternative Sets of Signals
Search Posting
0.45
Find
0.86
Look
0.87
Need
0.70
Search
0.61
0.77
Seek
0.46
Hiring
0.81
Job
0.82
Work
2.57
2.14
Variance
0.53
Fraction of variance 0.51
Note: Table shows the factor loading for the first factor for the selected signals. The bottom two rows
report the variance of the first factor and the fraction of the overall variance accounted for by the first
factor.

Table 11. Unobserved Components Model of Job Loss
Component
Parameter

Estimate
3.0

True
(2.2)
11.2
 1
Noise, Initial Claims
(1.0)
5.0
 2
Noise, Social Media Index(0.7)
Note: Sample period is July 16, 2011 through November 2, 2013. Estimates by method of moments as
described in text. Standard errors in parentheses.

Appendix Table 1. Social Media Signals: Job Loss and Unemployment
Number of distinct
Category
SignalPhrasematched phrases
1
Job loss
Axed
axed
1
Cannedcanned1
Downsized
downsized
1
down|sized1
Outsourced
outsourced
1
Pink slip
pinkslip
1pink|slip
Lost job
Firedlost|*|job45
fired|*|job28
fired|*|work
16
1
fired|from1
fired|lol1
get|fired1
got|fired1
just|fired
1
Been fired
been|fired
1
being|fired1
be|fired1
was|fired1
Laid off
laidoff
1
laid|off1
layed|off1
layoff1
lay|off
Unemployment
Unemployment
unemploy11
unemployed
1
unemployment
Note: The signals are counts of Tweets that contain 4-grams with the indicated phrase where “|” denotes
a space and “*” is a wildcard. The last column indicates the number of distinct phrases found in the
database of Tweets matching the target phrases with wildcards. See text for details.

Appendix Table 2. Social Media Signals: Job Search and Job Posting
Number of distinct
Category
SignalPhrasesmatched phrases
242
SearchFindfind|*|job
178
find|
*|work237
Look
look*|*|job497
look*|*|work398
Need
need|*|job
515
need|
*|work93
Searchsearch*|*|job38
search*|*|work
30
Search andSeek
seek*|*|job11
Posting
seek*|*|position29
seek*|*|work17278
Posting
Hiringhiring|*1
Job
job|opportunities
1
job|
opportunity
3040
jobs|
in|*
36
jo
bs|near|*
4397
job|
in|*
18
job|
near|*
10163
Work
work|in|*32
wo
rk|near|*1
work|opportunities
1
work|
opportunity
Note: See Appendix Table 1.

Appendix Table 3. Signals by Age and Sex

Age

14-18
19-21
22-24
25-34
35-44
45-64
Total

Fraction of Signals (percent)All
Job loss
Search
20.9
12.3
37.1
8.2
5.6
13.0
10.1
9.0
12.4
14.5
15.2
14.0
13.1
15.9
8.3
33.2
42.1
15.1
100.0
100.0
100.0

Posting
9.6
5.4
6.8
11.0
14.5
52.7
100.0

Male
60.6
66.7
50.5
59.8
Female
39.4
33.3
49.5
40.2
Total
100.0
100.0
100.0
100.0
Table shows fraction of job-related signals by age and sex of sender. The demographics are estimatedprobabilistically and are coded for only a subset of signals. Because of changes in the API, this sample
ends June 15, 2013.
Sex

Figure 1. Twitter Job Loss and Unemployment Signals
A. Factor 1
20
Factor 1

15

Factor

10

5

0

-5

-10
2011

2012

2013

B. Factors 1 – 4
20
Factor 1
Factor 2
Factor 3

15

Factor 4

Factor

10

5

0

-5

-10
2011

2012

2013

Note: Sample period is July 16, 2011 through November 2, 2013 (weeks ending Saturday).
Principal component factors calculated based on the correlation matrix of signals shown in Table
2.

Figure 2. Initial Claims for Unemployment Insurance and Job Loss and Unemployment Factor 1

460

20
Initial Claims (lef t scale)Social Media (right scale)

440

15
420
10

380
5
360
340

Factor 1

Thousands

400

0

320
-5
300
280

-10
2011

2012

2013

Note: Figure shows the Department of Labor’s Initial Claims for Unemployment Insurance (left scale, revised data, seasonallyadjusted) and the Social Media Factor 1 (right scale). The factor is estimated as described in the text and is no way fit to the initialclaims data.

Figure 3. Initial Claims for Unemployment Insurance and the Social Media Job Loss Index

460
Initial Claim s
Social Media Index

440
420

Thousands

400
380
360
340
320
300
280
2011

2012

2013

Note: Figure shows the Department of Labor’s Initial Claims for Unemployment Insurance and the Social Medial Job Loss Index.
The Social Media Job Loss Index is estimated in sample in the shaded area and recursively thereafter. See text for details.

Figure 4. Surprises Predicted by Social Media Job Loss Index
A. Preliminary Data
80

Surprise
Predicted with Social Media Index

60

Thousands

40

20

0

-20

-40
2011

2012

2013

B. Revised Data
100

Surprise
Predicted with Social Media Index

80

Thousands

60
40
20
0
-20
-40
2011

2012

2013

Note: Surprise is Department of Labor Initial Claims for Unemployment Insurance (preliminary
or revised) minus the consensus forecast. Predicted with Social Media Job Loss Index
constructed based on factor 1, as described in the text. The index is generated recursively except
in the shaded area, where it is generated over the entire shaded sample.

Figure 5. Social Media Indexes for Job Search and Job Posting
7.5
SearchPos ting

5.0

Social Media Index

2.5
0.0
-2.5
-5.0
-7.5
-10.0
-12.5
2011

2012

2013

Note: Indexes are based on factor loadings in second two columns of Table 10. The social
media indexes are estimated in sample in the shaded area and recursively thereafter.

Figure 6. Beveridge Curve
A. Search
5.0

Aug 6, 2011

Search Index

2.5

0.0
Jul 14, 2012

-2.5

Nov 17, 2012
Dec 24, 2011

Apr 20, 2013

-5.0

Nov 2, 2013

-7.5
340

350

360

370

380

390

400

Job Loss Index

B. Posting4
Aug 6, 2011

Posting Index20

Dec 24, 2011

Jul 14, 2012

Nov 17, 2012-2
Apr 20, 2013-4

Nov 2, 2013

-6
340

350

360

370

380

390

400

Job Loss Index

Note: Figures show the four-week moving averages of the Social Media Job Loss Index versus
the Search and Posting indexes.

Figure 7. Social Media Signal Related to Hurricane Sandy
400
Total (thousands)
Job loss and unemployment

350

Search and posting300

Signals

250
200
150
100
50
0
1

8

15
October

22

29

5

12

19
November26

Figure 8. Unobserved Job Loss versus Indicators
450
Unobserved Job Loss
Initial Claims
Social Media Index

425

Thousands

400

375

350

325

300
2011

2012

2013

Note: The red line is the estimated unobserved job lost calculated using the Kalman filter as described in the text. The black line is
new claims and the blue line is the social media index. The shaded area (November 2, 2013 to April 19, 2014) indicates data that are
not used to estimate the parameters for the Kalman filter.