# Controversy and Sentiment in Online News

Yelena Mejova∗ Amy X. Zhang‡ Nicholas Diakopoulos† Carlos Castillo∗

∗Qatar Computing Research Institute, †University of Maryland, ‡MIT CSAIL
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

How do news sources tackle controversial issues? In this work, we take a data-
driven approach to understand how controversy interplays with emotional
expression and biased language in the news. We begin by introducing a new
dataset of controversial and non-controversial terms collected using
crowdsourcing. Then, focusing on 15 major U.S. news outlets, we compare
millions of articles discussing controversial and non-controversial issues
over a span of 7 months. We find that in general, when it comes to
controversial issues, the use of negative affect and biased language is
prevalent, while the use of strong emotion is tempered. We also observe many
differences across news sources. Using these findings, we show that we can
indicate to what extent an issue is controversial, by comparing it with other
issues in terms of how they are portrayed across different media.

##  1 Introduction

One of the most vital functions of the news media is to serve as a place to
critically examine and present information about social, political, economic,
and ideological issues of the day. Many of these issues are controversial, in
the sense that they provoke arguments in which people express strong opposing
opinions [10].

For this reason, journalists must often take special precaution and make
careful language choices when they write about controversial issues. This can
often manifest as different ways of using language, for instance news sources
will often use a series of terms to signal that a controversy exists, such as
“outcry,” “furor,” and “uproar” [6]. It has been theorized that journalists
can also become susceptible to the ideologies, attitudes, and pressures of
their organization [13], as well as unstated rules and norms [8]. These inputs
can influence the particular language used to discuss controversial issues
within a particular news source. The difference in framing could be subtle
enough to be unnoticeable to the casual reader. However, using computational
techniques in textual analysis, we can analyze large datasets of articles for
consistent differences in the way different news organizations write about
controversial issues.

Our contribution. In this work, we quantify the use of emotional and biased
language when presenting controversial issues in the news. We begin by
building a list of controversial and non-controversial terms in current news
in the U.S. using crowdsourcing techniques. Then, we perform a large-scale
analysis of millions of news articles from 15 U.S.-based news sources. We
focus on the expression of sentiment using a series of lexical resources
containing words conveying positive and negative emotions; this automatic
analysis helps reduce the inherent subjectivity of traditional content
analysis methods.

We demonstrate that controversial issues in news can be characterized by the
use of fewer positive words and a greater presence of negative words. This
finding is consistent across different media sources and confirmed with 4
different sentiment lexicons. Interestingly, we find that the use of highly
emotional terms (as opposed to mild ones) is _less_ likely in the context of
controversial topics, suggesting a self-moderation on the part of the news
sources.

Additionally, we perform an analysis based on a vocabulary of words signaling
bias obtained from discussions in Wikipedia, and find that these bias terms
also tend to occur more frequently in articles mentioning controversial
topics, and can serve as a fairly accurate predictor of the level of
controversy.

The next section outlines previous work related to controversy in news media.
Next, we describe our dataset of online news (Section 3) and describe the
process we used to label strongly controversial, somewhat controversial, and
non-controversial words (Section 4). We then compare controversial and non-
controversial articles in terms of a series of bias and sentiment lexicons in
Section 5, and discuss the differences in the strength with which annotators
perceive a topic as controversial and the treatment it received in news media
in Section 6. Lastly we discuss the implications and limitations of such
computational approaches to media analysis.

##  2 Previous Work

Controversy has been examined in both social media, including Wikipedia and
Twitter, and more traditional news sites.

The unique structure of Wikipedia as a collaborative endeavor has been used by
Rad and Barbosa [11] who detect controversy based on mutual reverts, bi-
polarity in the collaboration network, and/or mutually-reinforced scores for
editors and articles (controversial editors work in controversial articles).
The fact that an article on Wikipedia is controversial has then been used to
evaluate the level of controversy of other documents (e.g. web pages) by
mapping them to related Wikipedia articles [9].

Taking a content-driven approach, Pennacchiotti and Popescu [10] detect
controversies around celebrities in Twitter. They use a number of features
including the presence of sentiment-bearing words, swear words, and words in a
list of controversial topics from Wikipedia.

Controversies in online news. In news sites, Choi et al. [5] detect
controversial issues by looking at words that frequently appear in contexts
containing positive and/or negative sentiment words. Also using lexicons,
Chimmalgi [4] study controversy in user comments of news articles. Taking a
more data-driven approach, Awadallah et al. [1] describe a method in which
opinion holders and their opinions in news articles are identified by an
iterative method based on a seed set of patterns that describe expressions of
support or opposition to an idea.

In contrast to previous work that applies sentiment analysis to controversies,
we do not assume that sentiments and controversy are related, but instead
demonstrate it experimentally, thus not only providing an empirical basis for
future use of sentiment lexicons, but also discovering new insight into
potential self-moderation of the news sources.

##  3 News Articles

Data was provided by NewsCred,111http://newscred.com/ which aggregates news
content from thousands of news sources, and makes it available via an API
(described further in Diakopoulos et al. [7]). We chose 15 high-volume sources
from NewsCred and considered their articles in the period from March to
September 2013. The main criterion we used for this selection was
variety—including both national and regional sources—while at the same time we
attempted to keep the list relatively brief for ease of analysis and
exposition:  CNN,  Reuters,  Usa Today,  Los Angeles Times,  Washington Post,
Chicago Tribune,  News Day,  Minneapolis Star Tribune,  Houston Chronicle,
Philadelphia Inquirer,  Honolulu Star-Advertiser,  Huffington Post,  New York
Times,  ProPublica, and  Talking Points Memo.

Two data gathering tasks were done. First, we collected a random subset of
articles for the purposes of obtaining high-frequency words. This is the list
of initial topics used for the labeling task described in Section 4. Second,
we searched the selected media sources for articles containing each topic word
using the NewsCred API. In total, over 21 million articles were collected,
with an average of around 3,000 and median of 1,000 per topic per news source.
Reuters was the most prolific source with an average of 11,659 articles per
topic, and ProPublica the least at 31 articles.

##  4 Controversial Words

Whether a word is controversial or not is a highly subjective and context-
dependent matter. Since controversy is socially constructed, we performed an
annotation effort with a relatively large pool of annotators. We employed 25
annotators based in the U.S., hired through crowdsourcing platform CrowdFlower
(https://crowd-flower.com/) and paid $0.80 (USD) for every 100 words
annotated, following standard pricing practices of this platform.222In total
40 annotators participated in the task, but only 25 of them contributed more
than 100 labels. This is common in crowdsourcing platforms.

Initial Words. The initial list was composed of the top frequent words in a
large random sample of articles. We filtered this list to keep only single-
word nouns using a part-of-speech tagger. Next, we removed generic English
stopwords, as well as frequent news-specific stopwords (ap, broadcast, press,
published, rewritten, redistributed, rights, copyright, reserved), and kept
the top 2,000 most frequent terms.

Crowdsourced annotation. We used a four-point scale to classify 2,000 high-
frequency terms, asking 7 “trusted” annotators per word the following
question:

> You need to be familiar with U.S. news media. Indicate if word is:
>
>   * (C3)
>
> Strongly Controversial: people often disagree and debate with opposing
> viewpoints.
>
>   * (C2)
>
> Somewhat Controversial: people sometimes disagree or have debates with
> opposing viewpoints.
>
>   * (C1)
>
> Less Controversial: people infrequently disagree or have debates with
> opposing viewpoints.
>
>   * (C0)
>
> Non-Controversial: people almost never disagree or debate with opposing
> viewpoints.
>
>

“Trusted” annotators are selected by including a set of terms for which the
label was known. These terms were obtained by a preliminary task in which we
asked 5 crowdsourcing workers for each of 1,000 words whether they believed it
to be controversial in U.S. news media or not. We selected a balanced set of
94 words for which there was perfect agreement in the preliminary task and
assigned them to C0 and C1 (for non-controversial terms) and C2 and C3 (for
controversial terms). Annotators who did not agree substantially with this
gold standard were not considered “trusted.”

Table 1: List of words identified during the crowdsourcing task. Strongly
Controversial (145): abuse, administration, afghanistan, aid, america,
american, army, attack, attacks, authorities, authority, ban, banks, benefits,
bill, bills, border, budget, campaign, candidate, candidates, catholic, china,
chinese, church, concerns, congress, conservative, control, country, court,
crime, criminal, crisis, cuts, debate, debt, defense, deficit, democrats,
disease, dollar, drug, drugs, economy, education, egypt, election, elections,
enforcement, fighting, finance, fiscal, force, funding, gas, government, gun,
health, immigration, inaccuracies, india, insurance, investigation,
investigators, iran, israel, job, jobs, judge, justice, killing, korea, labor,
land, law, lawmakers, laws, lawsuit, leadership, legislation, marriage, media,
mexico, military, money, murder, nation, nations, news, obama, offensive,
officials, oil, parties, peace, police, policies, policy, politics, poll,
power, president, prices, primary, prison, progress, race, reform, republican,
republicans, restrictions, rule, rules, ruling, russia, russian, school,
security, senate, sex, shooting, society, spending, strategy, strike, support,
syria, syrian, tax, taxes, threat, trial, unemployment, union, usa, victim,
victims, violence, vote, voters, war, washington, weapons, world
---
Somewhat Controversial (45): account, advantage, amount, attorney, chairman,
charge, charges, cities, class, comment, companies, cost, credit, delays,
effect, expectations, families, family, february, germany, goal, housing,
information, investment, markets, numbers, oklahoma, parents, patients,
population, price, projects, raise, rate, reason, sales, schools, sector,
shot, source, sources, status, stock, store, worth
Non-Controversial (272):  60s, 70s, addition, address, afternoon, agreed, amp,
angeles, answer, april, attention, avenue, average, ball, base, bay, beach,
beginning, bit, block, blue, bowl, box, boy, boys, brother, building, bus,
call, calling, calls, camp, car, cars, central, cents, click, close, cloudy,
club, coast, cup, dallas, date, daughter, davis, day, decade, decades,
december, def, delivery, door, download, drive, eagles, end, entire, era,
evening, face, faces, facility, fall, fans, father, feel, feeling, feet, fell,
field, finish, floor, form, fort, francisco, friday, friend, friends, fun,
girl, girls, ground, gt, guy, guys, half, hall, hand, hands, hawaii, heart,
heat, heavy, hill, hits, hold, hopes, host, hotel, hour, hours, house,
houston, hundreds, husband, ice, illinois, index, indiana, innings, island,
january, johnson, jones, june, kansas, kind, lack, lake, leave, lee, letter,
levels, light, line, lines, lot, lows, lt, main, make, makes, mark, mass,
material, matter, medium, men, mid, middle, miles, mind, minneapolis, minutes,
moment, monday, month, months, morning, mother, mountain, move, mph, museum,
names, natural, net, night, north, note, notes, november, october, opening,
park, part, parts, pass, period, person, philadelphia, pick, pitch, plant,
play, player, playing, pm, point, post, practice, put, quarter, rain, read,
reading, red, rest, restaurant, rise, rock, rose, round, sale, san, saturday,
scene, search, season, seasons, seconds, selling, september, series, set,
showers, showing, shows, sign, signs, smith, son, sox, special, spot, spring,
square, stadium, stage, start, starting, starts, station, stay, step, stores,
street, student, summer, sun, sunday, thing, things, thinking, thought,
thousands, thunderstorms, thursday, time, title, top, total, transportation,
type, unit, valley, vehicle, version, village, visit, wait, walk, wall, watch,
water, ways, wednesday, week, weekend, weeks, williams, wind, winds, winner,
winter, word, writer, yards, year, years, york

Finally, we considered only labels for which the majority label was larger
than 60% among the 7 workers. This yields Table 1 containing 145 controversial
terms having label C3, 45 terms having label C2, and 272 terms having label
C0. Class C1 is made of borderline cases and elicited very little agreement,
it was thus not considered.

Controversial terms in Table 1 are part political terms referring to congress
and legislation, social terms like education and unemployment, country names
including russia and china, and terms such as criminal and threat. Those in
the medium category may potentially belong to a controversial topic, such as
february being Black History Month in the U.S. Finally, the non-controversial
terms include mostly generic words.

##  5 Controversy and Sentiment

Using the dataset we describe above, we explore the vocabulary used in
articles mentioning controversial/non-controversial topics using a series of
lexical resources related to sentiment and bias.

Method. The general methodology is to consider in turn each word $w$ in our
vocabulary of 462 topics, and each media source $s$ in our list of 15 news
media. Then, collect all articles in the source $s$ containing the word $w$,
eliminate duplicate and near-duplicate articles (typically a by-product of
articles that have multiple URLs), and combine all the articles in one topical
(and very large) super-article for analysis.

The analysis consists of examining this content using a lexical resource,
counting the proportion of words matching a certain category in the lexical
resource. We consider the following sentiment lexicons:

  1. a.

Affective Norms for English
Words333http://csea.phhp.ufl.edu/media/anewmessage.html (ANEW) is a set of
normative emotional ratings for 2,476 English words. We use the “valence”
rating considering positive (respectively, negative) the ratings above
(respectively, below) the mean.

  2. b.

General Inquirer444http://www.wjh.harvard.edu/~inquirer/homecat.htm is a list
of 1,915 words classified as positive, and 2,291 words classified as negative.

  3. c.

MicroWNOp555http://www-3.unipv.it/wnop/ [3] is a list of 1,105 WordNet
_synsets_ (cognitive synonyms) classified as positive, negative, or neutral.

  4. d.

SentiWordNet666http://sentiwordnet.isti.cnr.it/ [2] assigns to each synset of
WordNet (around 117,000) a positive and negative score determined by a
diffusion process.

Additionally, we use a bias-specific lexicon:

  * e.

Bias Lexicon777http://www.mpi-sws.org/~cristian/Biased˙language.html is a list
of 654 bias-related lemmas extracted from the edit history of Wikipedia by
Recasens et al. [12]. Sentiment words are used as contributing features in the
construction of this bias lexicon.

To assess how prominent bias- and sentiment-laden terms are in controversial
topics, we count the number of times each lexicon term is used in each super-
article, and divide this count by the total length of the article, resulting
in a proportion of the text which uses the lexicon terms.

Figure 1: Distributions of the proportion of biased, positive ($+$), and
negative ($-$) words in the Huffington Post (HUF), CNN, and Reuters (REU),
across controversial topics (C), somewhat controversial topics (W) and non-
controversial topics (N).

Bias, positive and negative emotions. The distributions of the proportion of
words matching each vocabulary are shown in Figure 1 in which for brevity we
have selected three media: Huffington Post (HUF), CNN, and Reuters (REU). As
we show in the next section, these three news media sources differ in the
extent to which the use of lexicon words are used around controversial words
in their articles (from most to least).

First, we observe that the use of bias terms is more likely in controversial
topics than non-controversial topics. This is statistically significant at
$p<0.01$ for all 15 news sources.

Second, the use of negative terms is more likely in controversial topics than
non-controversial topics. This is statistically significant at $p<0.01$ for 46
out of the 60 combinations of source and lexicon (15 sources and 4 sentiment
lexicons), with 9 ties (no difference significant at this level in either
direction) and 5 cases in which the difference is significant at this level in
the opposite direction.

Third, the use of positive terms is more likely in non-controversial topics
than controversial topics. This is statistically significant at $p<0.01$ for
40 out of the 60 conditions, with 16 ties and 4 cases with a significant
difference in the opposite direction.

Strong emotions. Three of our lexicons (ANEW, MicroWNOp and SentiWordNet)
include scores that allow us to distinguish between weakly and strongly
emotional terms. We observe that the use of strong emotional words is less
likely in controversial topics. This is statistically significant at $p<0.01$
for 32 out of 45 conditions (15 sources and 3 sentiment lexicons), with only 1
of the remaining conditions having a significant difference in the other
direction.

Differences across sources. We find a great variety in the different treatment
that controversial and non-controversial topics have, in terms of the use of
biased and emotional words. In terms of statistical significance, the clearer
difference between controversial and non-controversial topics was observed
using  (i) the lexicon of bias words,  (ii) the General Inquirer strong
sentiment words, and  (iii) the ANEW negative words.

We next rank the media sources in terms of their different usage of words in
these lexicons in controversial and non-controversial topics. The top 5
sources for each one are shown on Table 2. We note that several media sources
repeat in this list, with Huffington Post, Washington Post, and New York Times
remaining on top, indicating a consistently large difference in their usage of
sentiment words around controversial topics, compared to non-controversial
ones.

Table 2: Top news sources in terms of the difference in the use of bias/emotional words in controversial and non-controversial topics. | Bias |  Gen Inq $Strong$ |  ANEW $-$
---|---|---|---
1. | Huffington Post | Huffington Post | Huffington Post
2. | Washington Post | Washington Post | USA Today
3. | New York Times | New York Times | New York Times
4. | LA Times | CNN | Washington Post
5. | USA Today | LA Times | LA Times

##  6 Ranking Controversy Words

Finally, we assign a score to each topic in our list of controversial and non-
controversial terms by using logistic regression, using as input features the
proportion of words from each lexicon in each news source, and as training
data the manually-labeled words (using only the classes C3 and C0 that
represent the extreme values). This is done using feature selection, selecting
5 features out of the total 195, and training a logistic regression
classifier. Then, the same classifier is applied to the training data (the
purpose of this is not to generalize to unseen topics, but to understand the
existing one), and a score between 0 and 1 is computed for each word (0.0 is
non-controversial, 0.5 is undecided, and 1.0 is strongly controversial).

Figure 2 depicts the training errors, which appear above the horizontal line
in the plot for non-controversial topics and below it in the plot for
controversial topics. To put these errors into context, we include in the
figure the confidence of the manual annotation process. Note that most of the
terms are classified correctly, appearing at the bottom of non-controversial
and top of controversial figures. In the next section, we discuss the possible
reasons for the misclassification of some of the topics.

(a) Non-controversial words; correctly classified words appear below the
horizontal line. (b) Controversial words; correctly classified words appear
above the horizontal line.

Figure 2: Scores of controversial and non-controversial words including
classification errors. “User score” is the confidence with which the manual
labeling was done (with at least 7 annotators per element), while “classifier
score” is the output of the classifier on the training data.

##  7 Discussion

In this study, we found that controversial issues are often framed using
negative emotions. Many of the terms that were found to be controversial are
oftentimes related to social problems and violence, such as gun, fighting,
crime, victim and shooting, or war, such as strike, weapons, and army. There
may not be as many opportunities to discuss some of these issues in a non-
negative light. Other terms could be viewed in a negative, positive, or even a
neutral light depending on the context at hand, for instance, terms such as
health, security, or jobs. However, because these words often invoke larger
ideological issues of government spending, privacy, or the economy, it may be
that frames emerge that are more antagonistic. For instance, instead of
emphasizing the positive aspects of one’s view, a writer may choose to
highlight the negative aspects of the opposing view.

Additionally, we found that controversial topics also involve less strongly
emotional words. Theories of framing posit that various organizational
pressures can shape the frames different news sources employ [13]. This may
point to an effort on the part of news agencies to more tightly control the
language used when discussing controversial topics. As an overt example of
organization influence on language use, many organizations today adhere to
different well-known style guides when choosing certain language around
sensitive topics. The use of different style guides may point to some of the
similarities and differences we saw between sources and certain controversial
words. Finally, the use of biased terms become more prevalent around
controversial words, with abstract notions of fairness like justice and
rights, and judgment-laden terms like terrorist and criminal having a high
correlation.

Based on the above observations, we built a model to estimate to what degree a
news source is treating a term as controversial. We find that some strongly
controversial terms are instead classified as non-controversial—they appear in
the bottom right corner of Figure 2(b). In some cases, terms that have
different connotations depending on the context may have lead to incorrect
classifications. For instance, oil, when referenced in a financial article may
treat the subject objectively as opposed to using biased or emotional
language. Another example is drug which can sometimes simply reference drug
stores. However, the classification of other less ambiguous terms, such as sex
and killing as non-controversial prompts re-examination of these topics as
controversial. Further work is needed to model more clearly the topics
represented by these terms in order to better understand their context.

The approach described in this work also allows us to examine the language of
each agency around a controversy using a controlled vocabulary. For example,
for the term democrats we compare the top 30 terms of the bias words lexicon
used by each agency. We see that, along with more topical terms like obama and
democratic, Huffington Post also includes more general and subjective terms
like very and good, unlike, say, CNN and Reuters. The lexicons also allow us
to glimpse a more local approach to news of smaller news agencies compared to
national ones. When discussing _murder_ , Reuters and CNN often mention a
larger framework of government and other groups, whereas Philadelphia
Inquirer, Honolulu Star-Advertiser, and Houston Chronicle mention particular
people (woman, victim) and places (university, west). Further development of
lexical resources for deeper understanding of news coverage beyond sentiment
is an exciting future direction of this research.

The large-scale analysis we have conducted is an initial inquiry into
quantifying how news sources differ in their framing. By being able to pick
apart how news sources differ in often subtle ways, we can begin to uncover
implicit biases in framing or language use by different news entities. That
many of these news sources are quite large, employing hundreds and thousands
of writers and editors, suggests that organization-level pressures to conform
to a particular standard or world view may in fact exist. This work can also
be used to inform automatic style guide checkers, serve as a reference to
journalists interested in maintaining objectivity, or readers wishing to
monitor their news intake.

Data release: our list of strongly controversial, somewhat controversial and
non-controversial terms, along with their scores is available at
www.yelenamejova.com/resources/controversial_words.txt.

Acknowledgments: the authors would like to thank NewsCred for making the data
available for our research.

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# Flexible experimental platform for dispersion-free temporal characterization
of ultrashort pulses

Patrick Rupprecht  1,2,* Alexander Magunia  1 Lennart Aufleger  1 Christian
Ott  1 and Thomas Pfeifer1,3  1Max-Planck-Institut für Kernphysik,
Saupfercheckweg 1, 69117 Heidelberg, Germany
2Currently with Lawrence Berkeley National Laboratory, 1 Cyclotron Road,
Berkeley, CA 94720, USA
3Center for Quantum Dynamics, Ruprecht-Karls-Universität Heidelberg, Im
Neuenheimer Feld 226, 69120 Heidelberg, Germany.  *patrick.rupprecht@mpi-
hd.mpg.de

††journal: opticajournal††articletype: Research Article

The precise temporal characterization of laser pulses is crucial for
ultrashort applications in biology, chemistry, and physics. Especially in
femto- and attosecond science, diverse laser pulse sources in different
spectral regimes from the visible to the short-wavelength infrared as well as
pulse durations ranging from picoseconds to few femtoseconds are employed. In
this article, we present a versatile temporal-characterization apparatus that
can access these different temporal and spectral regions in a dispersion-free
manner and without phase-matching constraints. The design combines transient-
grating and surface third-harmonic-generation frequency-resolved optical
gating in one device with optimized alignment capabilities based on a
noncollinear geometry.

##  1 Introduction

Ultrashort laser pulses are enabling crucial applications in many different
fields of industry and science: from micromachining [1] over multi-photon
microscopy [2] to attosecond physics and chemistry [3]. The temporal
characterization of such laser pulses has presented a significant challenge
since their first generation in the second half of the 1960s [4, 5, 6, 7]. As
their duration down to the femtosecond timescale is orders of magnitude
shorter than the fastest available electronics, multiple approaches have been
developed to address this issue: One class of setups relies on measuring the
pulse with itself by means of optical nonlinearities. Amongst these techniques
are autocorrelation [8], frequency-resolved optical gating (FROG) [9, 10],
SPIDER [11], 2DSI [12], MIIPS [13], ptychographic reconstruction [14] and the
dispersion-scan approach [15]. Further techniques rely on an in-situ
characterization of the ultrashort pulse in strong-field experiments inside a
vacuum environment such as streaking [16] or core-level transient absorption
spectroscopy [17]. More recently, tunnel ionization in air has been employed
for pulse characterization purposes [18, 19]. These techniques and their
implementations are usually optimized to a specific set of laser parameters
like center wavelength, bandwidth, peak intensity, pulse duration, and chirp.
Nowadays, state-of-the-art table-top setups for ultrafast dynamics experiments
rely on multiple laser sources in different wavelength, peak-power and pulse-
duration regimes [20, 21, 22]. Hence, a common temporal characterization of
all available laser-pulse sources imposes a significant challenge to everyday
lab operations in modern ultrafast science and especially in attosecond
science: In general, the laser front-end is based on the Titanium-Sapphire
(Ti:Sa; around 800 nm center wavelength) [23] or the Ytterbium (around 1030
nm) technology [24] with primary pulse durations ranging from roughly 20 fs to
1 ps. These driving pulses are often combined with an optical parametric
(chirped) pulse amplification [25] and a nonlinear compression stage [26, 27]
to reach few-cycle pulse durations or even sub-cycle light transients [28].
Recently, the goal of table-top soft x-ray generation has led to the
development of sources in the short-wavelength infrared regime (SWIR; 1 – 3
µm) due to the ponderomotive scaling in the underlying high-order-harmonic
generation (HHG) process [29, 30]. As laser pulse sources for attosecond
science rely heavily on (extremely) nonlinear optical effects like HHG,
temporal pulse-shape characterization and optimization in every stage of the
laser system is highly desirable. One example includes the HHG of isolated
attosecond pulses versus attosecond pulse trains depending on the temporal
duration and structure of the driving pulses. This illustrates the extent of
different wavelength and pulse-duration regimes involved in state-of-the-art
attosecond science. While separate pulse-characterization setups for every
component of the respective laser system might be commercially available, a
single cost-efficient temporal characterization device which is straight-
forward to use and covers all these different pulse sources remained elusive
so far. In this article, we present such a versatile temporal-characterization
apparatus capable of precisely measuring pulses from pico- to few-femtosecond
duration and from the visible to the SWIR spectral regime and beyond.

##  2 Experimental apparatus

The pulse-characterization apparatus combines transient-grating (TG) [31] and
surface third-harmonic-generation (sTHG) FROG [32] in a single device.
Changing between these two modi is easily conducted via a simple and fast
realignment procedure. Due to the selection of the sTHG and the TG nonlinear
process, no phase-matching constraints apply and hence no fundamental
restrictions concerning spectral applicability of the pulse-characterization
device exist. Furthermore, the apparatus is virtually dispersion-free by
relying on reflective optics in combination with a thin optical target. This
guarantees an accurate measurement of pulses with few- to single-cycle
duration. The TG approach with its degenerate $\chi^{(3)}$ signal and
intuitive traces is suitable for pulses in the visible and near-infrared
spectral regions. Complementary to that, the sTHG nonlinearity as a
nondegenerate four-wave-mixing process is ideal for pulse characterization
purposes in the SWIR regime since the measured third harmonic is accessible
with cost-efficient silicon-based detectors instead of expensive indium-
gallium-arsenide (InGaAs) equipped spectrometers. An illustration of the
optical beam path of the versatile pulse-characterization apparatus is
presented in Fig. 1.

Figure 1: Beam path of the pulse-characterization apparatus. The input laser
pulses enter from the top right and are aligned on apertures Ap1 and Ap2. To
mimic additional dispersive materials in the ultrafast experimental setup
and/or to attenuate the beam, windows/ND filters can be inserted in the beam
path. Routing mirrors M1 and M2 as well as the one-quadrant mask (mask1; see
inset with beam-mode overlay) are realigned to switch between the different
pulse-measurement modi. Two D-shaped mirrors (one of them motorized) form the
split-and-delay unit (S&D). The focusing mirror FM1 is equipped with an
exchangeable four-hole mask (mask2; see Fig. 2 for details) which splits the
beam mode into beamlets. The respective four (alignment mode), three (TG mode)
or two (sTHG mode) beamlets are focused into a thin target medium (tar). A
magnetic-base mounted mirror (magM) can be inserted to re-route the beams on a
camera (cam) to determine the spatial/temporal beam overlap in the target.
After the optical target, the beamlets (including the signal beamlet) are
routed via mirror M3 and recollimated with the focusing mirror FM2 (the
beamlets are shown here for the TG mode). A movable aperture (Apxy) isolates
the nonlinear signal and an off-axis parabolic mirror (OAP) focuses the signal
into a multimode optical fiber which is connected to a spectrometer.

The overall pulse-characterization device is realized on a 30 cm $\times$ 45
cm breadboard. In this optical setup, two parts of the input beam mode are
time-delayed with respect to each other by two flat D-shaped mirrors. One of
them is moved by a piezoelectric slip-stick actuator (SmarAct CLS-3232) with a
21 mm travel range at a 1 nm closed-loop sensor resolution. For alignment of
this split-and-delay unit (S&D in Fig. 1), a magnetic-based mirror (magM in
Fig. 1) re-routes the beam and images the target focus onto a camera (cam in
Fig. 1). The relative timing of the upper and the lower half of the beam mode
are controlled with sub-femtosecond precision over a picosecond range via the
spatial displacement of one of the two D-shaped mirrors. In consequence, the
temporal range is limited to 30 ps only by the spatial walk-off of the foci in
the target due to the slightly noncollinear reflective geometry of the S&D
unit. The spatial and the temporal overlap of the beamlets at the target
position are precisely adjusted by measuring the respective interference
pattern with a camera (cam in Fig. 1). In the next step, the beam mode is
spatially split into beamlets with a four-hole mask as shown in Fig. 2(a) and
2(c).

Figure 2: Geometry and mechanism of transient-grating and surface third-
harmonic-generation mode. Input beam position on the mask (compare mask2 in
Fig. 1) and schematic beam path for (a) the TG and (c) the sTHG mode. Wave-
vector schematic, nonlinear-signal generation and spectrally dispersed
measurement in (b) TG and (d) sTHG configuration.

Depending on the chosen measurement mode, the beam is aligned on the center of
the mask (TG mode) or between two vertical holes (sTHG mode). This alignment
is conducted using two routing mirrors (M1 and M2 in Fig. 1). In addition, a
one-quadrant mask (mask1 in Fig. 1) is rotated/shifted to ensure the correct
beam-mode cutting shown in Fig. 2(a) and 2(c). The respective TG or sTHG
signals are spatially isolated with an xy-movable, continuously variable iris
(Apxy in Fig. 1). Thus, switching between sTHG and TG mode is performed by a
simple re-alignment procedure within a few minutes without major changes to
the pulse-characterization setup. If the mask1 is completely flipped out of
the beam path, the fourth beamlet appears and can be used as pseudo signal for
TG-mode alignment all the way to the spectrometer input. This simplifies the
initial alignment of the device significantly. The four-hole mask is
exchangeable to take account of different beam mode sizes. For an easy and
reproducible placement it is directly mounted in front of a focusing mirror
(FM1 in Fig. 1).

In TG mode, one beamlet is time-delayed with respect to a pair of beamlets
that is generating the transient grating in the target medium. The
noncollinear BOXCARS design [33] shown in Fig. 2(a) results in the measured
dispersion-free transient grating spectrogram [34]

$I_{FROG}^{TG}(\omega,\tau)=\left|\int_{-\infty}^{\infty}E(t-\tau)\left|E(t)\right|^{2}\,e^{-i\omega
t}\,dt\right|^{2}$ (1)

with the angular frequency $\omega$, the time delay between the pulse beamlets
$\tau$, and the time-dependent complex electric field of the laser pulse
$E(t)$. This signal appears at the position of the virtual fourth corner of
the BOXCARS-geometry square. As the respective TG signal is a degenerate four-
wave mixing process, the phase-matching condition is automatically fulfilled
and hence an ordinary, thin glass plate can be used as target medium.
Therefore, no expensive and phase-matching-limited nonlinear crystal is
necessary. Due to the resulting unambiguous and intuitive measurement trace,
the TG mode is preferred whenever a suitable spectrometer for the respective
fundamental wavelength range is available.

In the sTHG mode, the beam realignment results in two noncollinearly focused
beamlets [see Fig. 2(c)]. By focusing the beamlets on the front surface of a
comparably thick target the dispersion-free character of the measurement is
maintained while a suppression of the sTHG signal originating from the
backside of the target is achieved. The sTHG signal is described by

$I_{FROG}^{THG}(\omega,\tau)=\left|\int_{-\infty}^{\infty}E(t)^{2}\,E(t-\tau)\,e^{-i\omega
t}\,dt\right|^{2}$ (2)

and will appear in between the transmitted input beamlets. In addition, a weak
interferometric surface second-harmonic generation (sSHG) [35] signal is
present in the middle between the two transmitted input beamlets. The sTHG
process translates the original spectral domain of the input pulse to 1/3 of
its wavelength. Thus, this process is ideally suited to characterize SWIR
pulses with a conventional silicon-based spectrometer that covers the
ultraviolet/visible (UV/VIS) range. Photo-detectors based on silicon are
limited to wavelengths smaller than $\sim$1100 nm due to the 1.1 eV bandgap of
silicon. The InGaAs-based alternatives that can access the > 1100 nm range,
however, they are cost intensive and require active cooling. While a linear
SWIR-related signal cannot be detected by silicon electronics, the respective
sTHG signal matches the spectral range of conventional UV/VIS spectrometers.
In principle, higher-order harmonics (e.g., surface fifth or seventh harmonic)
[36] can be used to translate even longer mid-IR wavelengths into the UV/VIS
regime.

##  3 Temporal characterization examples

The described pulse-characterization setup was built and tested with multiple
ultrashort-pulsed laser sources at the Max Planck Institute for Nuclear
Physics [37]. The respective sources provide amplified laser pulses in the
visible to SWIR regime with pulse durations ranging from several picoseconds
to the few-femtosecond regime.

###  3.1 Ti:Sa pulses from 27 fs to 8 ps

First, pulses from a commercial 1 kHz chirped-pulse-amplification Ti:Sa laser
system (Legend Elite Cryo; Coherent Inc.) were characterized. For these pulses
centered around 800 nm, the TG-mode of the pulse-characterization setup was
used in combination with a silicon-based, fiber-coupled spectrometer (Ocean-
FX-XR1; Ocean Insight). Fig. 3 shows the measurement and retrieval of the
compressed laser output.

Figure 3: TG-FROG measurements of near-Fourier-limited pulses from the Ti:Sa
laser system and comparison to a SPIDER measurement. (a) Measured (exp.) and
retrieved (ret.) TG-FROG traces. (b) Retrieved intensity and phase in the
spectral domain. (c) Retrieved intensity and phase in the time domain. Also,
the temporal pulse structure measured with a commercial SPIDER device is shown
(black dashed line) for comparison.

For this measurement, a $\sim 100\,\upmu$m-thin microscope cover glass (CG00C;
Thorlabs) was used as target. The pulse structure is retrieved using the
common pulse retrieval algorithm (COPRA) [38]. The uncertainty bands in the
spectral and temporal retrievals [Fig. 2(b) and 2(c)] are obtained by a
statistical analysis of the retrieval results originating from different
initial starting pulse guesses for the retrieval algorithm. For comparison,
these pulses were also characterized with a commercial SPIDER device (Compact
LX SPIDER; APE GmbH) as shown as dotted black line in Fig. 3(c). Here, the
temporal pulse structure including pre-/post-pulses from uncorrected higher-
order spectral-phase terms matches well for both devices. The retrieved full-
width-at-half-maximum (FWHM) duration of the FROG measurement of
$\tau_{FWHM}^{TG}$(800 nm) $=(26.9\pm 0.7)$ fs is slightly longer than the one
extracted from the SPIDER measurement ($\tau_{FWHM}^{SPIDER}$(800 nm) = 24.8
fs). The FWHM uncertainty of the FROG-deduced duration is given by combining
statistical deviations represented by the merely visible blue error band in
Fig. 3(c) with a systematic uncertainty of half a time-step size induced by
the measurement or the retrieval-algorithm rebinning (whichever value is
larger). A potential source for the small deviations in the retrieved pulse
structures resulting from the FROG and the SPIDER measurements is spatio-
temporal inhomogenity in the beam mode. While the collinear design of the
SPIDER device focuses on the center of the beam mode, the noncollinear BOXCARS
setup of the FROG apparatus employs off-center regions of the beam mode to
generate the measurement signal.

In a second step, to elucidate the capabilities of the versatile temporal-
characterization platform in the long-pulse limit, heavily chirped pulses from
a Ti:Sa laser system were characterized. In this case, a thicker target (1 mm
UV fused silica plate), which contributes only a negligible amount of
dispersion to extensively chirped pulses, was used. Apart from that, the same
setup configuration was used as for the compressed Ti:Sa measurement presented
above (TG-mode; UV/VIS spectrometer). The measurement result and retrievals
are shown in Fig. 4.

Figure 4: TG-FROG measurements of heavily chirped Ti:Sa pulses of picosecond
duration. (a) Measured (exp.) and retrieved (ret.) TG-FROG traces. (b)
Retrieved intensity and phase in the spectral domain. (c) Retrieved intensity
and phase in the time domain.

By detuning the grating compressor of the laser system, a group delay
dispersion of around GDD = $1.5\times 10^{4}$ fs2 and a third-order dispersion
of approximately TOD = $3.7\times 10^{4}$ fs3 was added to the pulse,
resulting in a retrieved pulse duration of $\tau_{FWHM}^{TG}$(800 nm chirped)
$=(8.3\pm 0.2)$ ps. The slight noncollinear angle of incidence (AOI) of the
S&D unit in Fig. 1 and the associated spatial foci walk-off in the target
limits the usable time-delay range to about 30 ps while the piezoelectric
actuator supports up to 140 ps. Hence, an optimization for > 10 ps FWHM pulse
durations by minimizing the mentioned AOI (e.g. by using a longer breadboard)
is easily feasible. Alternatively, a mechanical walk-off compensation via a
motorized mirror mount could be employed.

###  3.2 Short-wavelength-infrared few-cycle pulses

In order to showcase the accessibility of different spectral regimes,
broadband pulses in the SWIR spectral region were characterized utilizing
both, the TG and sTHG mode. The corresponding pulses are generated by
spectrally broadening the output of an optical parametric amplifier via self-
phase-modulation in a gas-filled hollow-core capillary and temporally
compressing the pulses by propagation through dispersion-compensating
materials [37].
First, a few-cycle pulse with a center wavelength of $\lambda$ = 1550 nm was
characterized using the TG-mode, a $\sim 100$ µm-thin microscopy cover-glass
target and an InGaAs spectrometer (NIRquest 512; Ocean Insight). The resulting
measurement trace and retrieval is depicted in Fig. 5(a) - (c).

Figure 5: TG-FROG versus sTHG-FROG measurement of few-cycle pulses with a
center wavelength of 1550 nm. For the TG measurement, an InGaAs spectrometer
was used while the sTHG data were recorded with a silicon-based UV/VIS
spectrometer. Measured (exp.) and retrieved (ret.) FROG traces of the TG (a)
and sTHG (d) measurements. Retrieved intensity and phase in the spectral
domain [TG - (b); sTHG - (e)]. Retrieved intensity and phase in the time
domain [TG - (c); sTHG - (f)].

Here, the pulses were compressed in the time domain utilizing negative
dispersion of fused silica (GVDFS(1550 nm) = -28 fs2/mm). By propagation
through additional 4 mm of fused silica, a pulse of three-optical-cycle
duration ($\tau_{FWHM}^{TG}$(1550 nm) = $(15.2\pm 0.5)$ fs) was achieved. The
residual TOD, visible in the red-dashed spectral phase in Fig. 5(b), is due to
the positive TOD of fused silica even in the SWIR region (TODFS(1550 nm) = 151
fs3/mm). To compare the different modi of the pulse-measurement apparatus with
each other, the same pulses were characterized using the sTHG scheme. The
corresponding results are presented in Fig. 5(d) - (f). Here, a thicker target
(2 mm z-cut MgF2; United Crystals) was aligned such that its front surface
coincides with the focus position [see Fig. 2(c)]. The same silicon-based
UV/VIS spectrometer as for the 800 nm measurements, described in section 3.1,
was used to detect the sTHG signal. Overall, the TG and the sTHG retrievals
match concerning the general temporal pulse structure and the retrieved FWHM
duration ($\tau_{FWHM}^{sTHG}$(1550 nm) = $(15.3\pm 0.5)$ fs). The differences
in the spectral domain are likely caused by inhomogenities in the beam mode
(e.g. spatial chirp) as the sTHG mask cuts different regions of the beam mode
compared to the sTHG mask [see Fig. 2(a) and (c)].

Due to the limitations of the available InGaAs spectrometer (spectral range
from 1.2 µm to 2.0 µm), longer-wavelength few-cycle pulses around 2000 nm
center wavelength are only accessible in the sTHG mode of the pulse
measurement device. Hence, Fig. 6 shows the resulting sTHG measurement trace
and retrievals of such an exemplary longer-wavelength few-cycle pulse
characterization. The measured pulse duration of $\tau_{FWHM}^{sTHG}$(2000 nm)
= $(15.8\pm 0.6)$ fs corresponds to a sub-three-optical-cycles pulse. This
temporal duration is achieved by propagation through additional 2 mm of
potassium bromide (GVDKBr(2000 nm) = 38 fs2/mm). Here, the positive GDD
introduced by the propagation through KBr is necessary to counteract the
negative dispersion by the unavoidable fused silica components in the beam
path.

Figure 6: Surface THG-FROG measurement of few-cycle pulses with a center
wavelength of 2000 nm. Here, a silicon-based UV/VIS spectrometer was used for
the measurement. (a) Measured (exp.) and retrieved (ret.) TG-FROG traces. (b)
Retrieved intensity and phase in the spectral domain. (c) Retrieved intensity
and phase in the time domain.

##  4 Conclusion

In this article, we presented a versatile temporal characterization platform
which is capable of measuring pulses from the few-femtosecond to the
picosecond regime and spanning a broad spectral region from the visible to the
SWIR. These capabilities were demonstrated by measuring compressed
($\tau_{FWHM}^{TG}$(800 nm) $=(26.9\pm 0.7)$ fs) as well as chirped
($\tau_{FWHM}^{TG}$(800 nm chirped) $=(8.3\pm 0.2)$ ps) pulses around 800 nm
from a Ti:Sa laser system in TG-FROG mode by utilizing a silicon-based
spectrometer. Furthermore, few-cycle pulses in the SWIR region were
characterized: The TG-mode was applied to a three-cycle
($\tau_{FWHM}^{TG}$(1550 nm) = $(15.2\pm 0.5)$ fs) pulse with a center
wavelength of 1550 nm. Here, the degenerate transient-grating signal was
measured with an InGaAs spectrometer and compared with a non-degenerate third-
harmonic-generation measurement which utilizes a conventional silicon-based
UV/VIS spectrometer ($\tau_{FWHM}^{sTHG}$(1550 nm) = $(15.3\pm 0.5)$ fs).
Also, a sub-three-cycle pulse ($\tau_{FWHM}^{sTHG}$(2000 nm) = $(15.8\pm 0.6)$
fs) around 2000 nm center wavelength was characterized using the sTHG mode and
a UV/VIS spectrometer. The presented pulse-characterization platform was
crucial for achieving the best possible compression of SWIR few-cycle pulses
by balancing their dispersion via propagation through dispersive media such as
fused silica (negative GVD for $\lambda<1270$ nm) and potassium bromide
(positive GVD for $\lambda<3830$ nm). The described pulse-characterization
device was successfully employed in the context of table-top time-resolved
x-ray absorption spectroscopy experiments: Here, the precise knowledge of the
temporal structure of the utilized SWIR pulses contributed to revealing
exchange-energy tunability in molecules [39] and to disentangling smallest
vibrational and electronic signatures in ultrafast molecular dynamics [40].
This highlights the importance of a universal pulse-characterization device
for interpreting the results of state-of-the-art ultrafast experiments at the
femto- and attosecond timescales. Furthermore, due to its transportability,
flexibility in operation and the straight-forward alignment, the pulse-
characterization setup can be easily operated for various laser systems and
laboratories and installed at different positions within an optical setup.

As this pulse-characterization platform is not limited by dispersion- or
phase-matching bandwidth constraints, its only limitation in regard to minimum
and maximum pulse durations as well as spectral applicability is determined by
the AOI of the split-and-delay unit, the noncollinear angle of the utilized
BOXCARS mask and the spectral reflectivity bandwidth of the mirrors. Hence,
this platform is suitable for the charaterization of pulses down to the deep
UV spectral region (e.g., around 200 nm): Here, pulse energies are typically
lower than in the discussed UV/VIS/IR cases making it necessary to split the
beam mode efficiently. This could be accounted for by utilizing a two-hole
mask and a self-diffraction scheme [41] in combination with UV-enhanced
aluminium mirrors. As the built-in piezoelectric actuator is vacuum
compatible, an in-vacuum use of the device is possible without further
modifications, which is crucial for UV pulse characterization purposes.
Moreover, a further miniaturization of the device is possible especially for
few-cycle, in-vacuum pulse characterization. On the other extreme of the
spectral scale, utilizing the sTHG mode with a conventional silicon-based
spectrometer grants access to pulses with spectra spanning up to $\sim 3300$
nm. Beyond this wavelength, cost-intensive but readily available InGaAs
spectrometers could be used to push the boundary to the 6000 nm mid-infrared
region and beyond. An alternative route to extend the measurement capabilities
towards the mid-infared is to utilize the surface fifth or seventh harmonic in
combination with a silicon-based UV/VIS spectrometer.

Funding We acknowledge financial support by the Deutsche
Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s
Excellence Strategy EXC2181/1-390900948 (the Heidelberg STRUCTURES Excellence
Cluster) and by the European Research Council (ERC) (X-MuSiC 616783). P.
Rupprecht acknowledges funding by the Alexander von Humboldt Foundation
(Feodor-Lynen Fellowship).

Acknowledgments The authors thank Nils Geib for helpful discussions concerning
the COPRA retrieval algorithm.

Disclosures The authors declare no conflicts of interest.

Data availability Data underlying the results presented in this paper are not
publicly available at this time but may be obtained from the authors upon
reasonable request.

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