The ‘golden years’ of paparazzi have mostly gone

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The ‘golden years’ of paparazzi have mostly gone

By Allison Schrager

While some exclusive celebrity photographs can command huge sums, earning a steady income in this controversial industry is getting a lot harder, writes Allison Schrager.

Santiago Baez has been a paparazzo since the early 1990s. Camera in hand, he’s witnessed the fallout of extramarital affairs, new babies, deaths, new love and breakups of some of New York’s most famous residents.

For paparazzi like Baez, earning a living requires an encyclopedic knowledge of where famous people live in New York, as well as a network of drivers, and shop and restaurant workers who call in tips when they spot celebrities in the vicinity. Often, the tips are from the celebrities themselves via social media: looking to build a following, they alert the public (mostly directed at photographers) about their movements, or their publicist will call an agency to dispatch a photographer.

Most pictures aren’t worth much, but a shot of a new baby, a celebrity kissing a new paramour, or a wedding can change fortunes overnight.

But Baez’s income is not dependably constant. His success balances his training and knowledge of celebrities with the crushing awareness that his earnings are remarkably variable and unpredictable.

The ‘paparazzi gold rush’

These fortunes are determined by a handful of people like Peter Grossman, the photo editor at Us Weekly from 2003 to 2017. But Grossman didn’t work with paparazzi directly; instead, a photographer like Baez sells his pictures to an agency that has the relationship with photo editors like Grossman. A paparazzo receives anywhere between 20% and 70% of the royalties the picture earns, depending on the photographer and the deal he or she negotiated with the agency. The more senior, skilled, and talented paparazzi command better terms, which often includes exclusively selling their pictures to just one agency.


In the heyday of paparazzi photographs, magazines like Us Weekly would pay thousands for an exclusive picture of celebrities doing mundane tasks (Credit: Alamy)

Exclusive shots that make waves in the world of tabloid news can command huge sums: Grossman told me he paid “mid six figures” for a series of photographs of the actress Kristen Stewart in a passionate embrace with Rupert Sanders, the married director of Snow White and the Huntsman, a film she had starred in.

Grossman lived through the heyday of paparazzi photography: he was the man behind the rise of “Just Like Us” pictures in the early 2000s – candid shots of celebrities doing mundane tasks like getting coffee or pumping petrol that proved a hit with his magazine’s readers. Soon, lots of outlets were publishing their own “Just Like Us” pictures, kicking off what’s known in the industry as the gold rush years, coinciding with the heyday of Paris Hilton, Britney Spears, and Lindsay Lohan.

At the gold rush peak, an exclusive ‘Just Like Us’ picture would typically fetch $5,000 to $15,000
Although the price of a photograph depended on what the celebrity was doing and whether it was an exclusive, at the gold rush peak, an exclusive “Just Like Us” picture would typically fetch $5,000 to $15,000.

The gold rush era brought about gold rush mentality, with many new photographers flocking to the industry, willing to break laws and giving paparazzi an even worse reputation for going too far and harassing celebrities and even their young children. Grossman urged everyone to take a coordinated step back, pay less for pictures, and not break laws or put themselves or others in danger to get the shot, but it didn’t work.

The global financial crisis and the rise of online media finally killed the gold rush. Digital media increased the demand for celebrity photographs but decreased the price media companies were willing to pay for them. Photo agencies began to consolidate or go out of business, and the remaining ones changed their business model. Instead of making magazines pay per photo, they offered a subscription service: publishers could use as many photos as they wanted to fulfill the greater demand for cheaper shots. As a result, paparazzi are paid a small fraction of the subscription fee; how much depends on how many of their pictures are used each month. That means an exclusive “Just Like Us” photo that would have fetched $5,000 to $15,000 before, now pays only $5 or $10.

Paparazzi are earning less and less. Gone are the days when many could count on a six-figure income. Now, getting a rare exclusive shot is necessary to earn big money.


To spread the risk around, paparazzi often form alliances – but those alliances can be upended by one big exclusive (Credit: Alamy)

Risky business

Seeing a celebrity often happens by chance, which is exactly part of the reason why Baez’s income is so volatile. Not surprisingly, Baez employs risk strategies in his craft similar to what people use in financial markets.

Financial economists separate risk into two broad categories: the first is idiosyncratic risk, or the risk unique to a particular asset. Suppose Facebook changes management; the future of the company is unclear, and the price of the stock might drop based on factors unique to Facebook that don’t impact any other stock. Idiosyncratic risk is risk that applies only to one individual stock or asset.

The paparazzi face lots of idiosyncratic risk. What a celebrity does today – whether she spends time with A-list or D-list friends, for example – determines how much the paparazzi earn that week. If a celebrity stops being interesting or popular, the value of these pictures decreases. Such images are like a stock: their value varies based on a particular photographer getting the right shot at the right time.

Photographers often form teams or alliances to share tips  to increase the odds they’ll be in that place
The paparazzi manage this idiosyncratic risk by spreading it around: photographers often form teams or alliances to share tips (on sightings) and sometimes royalties to increase the odds or payoffs they’ll be in that place.

Because each photographer bears lots of risk based on how lucky he is that day, an alliance pools their luck, reducing their idiosyncratic risk.

The second kind of risk is systematic risk, or risk that affects the larger system instead of an individual asset. Systematic risk is when every stock rises or falls together because the entire market surges or crashes as it did in 2008. Systematic risk events often happen because of a big economic disruption like a recession or an election result that people think will affect business. Systematic risks are harder to manage than idiosyncratic risks, and the downsides are potentially more dangerous. If the entire stock market tanks, you risk losing your job and stock portfolio at the same time.


There are curious parallels between Wall Street traders and the paparazzi. Both try to manage financial risk every day (Credit: Getty Images)

You can see systematic risk play out with paparazzi, like the boom of the gold rush years and the crash when people stopped buying tabloid magazines during the recession. The downside of systematic paparazzi risk has become more severe in the last 10 years. It is harder for everyone to make money. Many paparazzi have left the business: after nearly 30 years of taking celebrity photographs, Baez moved back to the Dominican Republic in the summer of 2018, with his wife and son, to find new work.

Paparazzi – just like us?

The job of a paparazzo is riskier than most. But to some extent we all face some level of idiosyncratic and systematic risk in our careers, so we can learn a lot from these photographers.

The more systematic risk associated with your job, the more exposed you are
Suppose you want to change jobs from a safe, salaried support role to a sales job based on commission. Odds are you’ll earn more than you did in the salaried job because as a salesperson you will face both kinds of risk: it is a job with loads of idiosyncratic risk; for example, how much you earn will depend on your sales skills and the behavior of your clients (you can manage this risk by working in a team and having lots of clients). You will also face systematic risk because sales depend on the state of the economy.

Systematic risk is especially dangerous. In an economic downturn, your pay may be reduced or disappear entirely, it is likely to be harder to find another job, your assets might take a hit, and your partner’s income may be at risk too. The more systematic risk associated with your job, the more exposed you are.

Why we feel so much economic anxiety

The livelihood of the average paparazzo is threatened by major changes in the publication industry. The photographers manage idiosyncratic risk by forming unstable alliances, but the larger systematic risk that could wipe out their jobs is harder to manage. They could form a union and demand better terms from the agencies, but historically they struggle to cooperate with one another. And the paparazzi are not the only ones who face the risk that their jobs will no longer be viable.

One reason people seem to worry more about their economic future than they did in the past is that they sense more systematic risk in the job market. A few decades ago, most of the employment risk was idiosyncratic: conflict with the boss, a position that was a bad fit, a poorly managed company. If you lost your job, you could probably find another one just like it. Workers formed trade unions, banded together, and demanded better pay and benefits, confident that there was a need for their skills. The job market had its ups and downs, but risk seemed to be relatively easy to manage.

In today’s economy, systematic risk is more acute. There’s a chance technology – robots and artificial intelligence – could take over your job or at least require new skills you don’t have. If you lose your job during a recession, you may never find a similar one.

It is a larger trend that threatens everyone, but for paparazzi like Baez, the threat is more immediate. It is a risky business that is only getting riskier with fewer rewards.

This article is adapted from An Economist Walks into a Brothel by Allison Schrager, published by Portfolio.
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https://www.bbc.com/worklife/article/20190423-how-the-paparazzi-make-their-money

How social media has created a new breed of paparazzi

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How social media has created a new breed of paparazzi

Paparazzi Miles Diggs (center) takes photos with his business partner, Cesar Peña (right).
It was the “get” of the week: the first picture of Hannah Davis’ engagement ring from Yankee icon Derek Jeter.

“I was looking for them since the engagement rumors started,” said paparazzo Miles Diggs, who runs 247Paps.TV with his partner, Cesar Peña.

They monitored Twitter and Instagram for sightings until “she finally slipped up,” Diggs said.

On Halloween night, Davis posted a photo to Instagram: her dressed as an angel in a pink wig; him in a devil costume.


Although she didn’t name the hotel, Diggs noticed distinctly patterned carpet and curtains in the background. He trawled Midtown hotel Web sites until he found rooms advertised with the same interior design.

The next morning, he posted himself outside the hotel.

“He came out first, then she finally came out and showed off that big ring,” Diggs recalled.

Snap! A $25,000 picture.

‘It’s all a hustle’
Diggs and Peña are a new breed of paparazzi, cursed and blessed by social media. Celebrity selfies and fan-snapped shots on the Internet have cut the prices they can charge for their pictures. But those tweets and Instagrams and Facebook posts allow them to track down and trail boldfaced names like never before.

By the time they get dressed, I’m already there.
 - Paparazzo Miles Diggs
“It’s all a hustle,” said Peña, 37, who lives in Harlem with his wife and 7-year-old daughter. He used to work in security at the Waldorf Astoria hotel before watching an E! network show, “Celebrity Uncensored,” which motivated him to pick up a $400 Canon video camera and start taping celebs in 2006.

Diggs, 21, joined him three years ago. Until then, he was a fan-turned-autograph hunter. He realized that while stars could turn him down for a signature, he didn’t need their permission to take their photo in public. He dropped out of studying photojournalism at NYU and has been working full time as a paparazzo ever since.

“A picture that now costs $400 was worth $15,000 to $20,000” before Instagram, Peña said. “It was harder to get pictures, harder to get stories.”


Miles Diggs (left) with his business partner, Cesar Peña (right).
When one of Nicki Minaj’s dancers posted a photo to Instagram of a rehearsal at a studio in Greenpoint, Brooklyn, with the caption “Feeling Myself” (the song Minaj recorded with Beyoncé), Diggs and Peña were able to get photos of both divas leaving the studio that night.

After Alicia Keys uploaded Instagram photos about training for the New York City Marathon, the paps headed to her New Jersey home to catch her out for a morning run.

When Kylie Jenner recently uploaded an Instagram photo of herself with model Hailey Baldwin in a fashion-shoot trailer, Diggs recognized the street and a shop in the background. He headed to the Bowery.

“We were sitting there behind the trailer before she even came out,” he said.

“Snapchat is a celebrity’s worst nightmare right now because I have an eye like a hawk,” Diggs said.

A star will post a picture of their hotel room’s view, and “by the time [they] get dressed, I’m already there,” he said.

The duo also searches Twitter for celebrity names or for phrases like “just saw Beyoncé,” “Jennifer Aniston is at” and “spotted Jennifer Aniston.”

Tricks of the trade
The practice of slipping $50 bills to doormen and bouncers for info is “a very LA mentality,” said Diggs, and not how the New York City ­paparazzi scene works.

Every day, Diggs and Peña do laps around Soho and the West Village, passing the major ­hotels — The Greenwich Hotel, The Mercer and the Trump Soho. They also have a mental map of the stars’ haunts and their homes — Taylor Swift and Beyoncé in Tribeca, Kim Kardashian and Kanye West in Soho, Rihanna in Chinatown.

Many of their tips come from what Diggs and Peña call their “little agents,” a half-dozen teens and college kids they’ve befriended who stalk celebs to take selfies with them.

No cash is exchanged, but Diggs and Peña will tip off the little agents when they spot their favorite celebrities.

“Their cut is being part of my crew. And they understand that,” Peña said.

“We have different agents for different people,” Diggs said.

“If they’re going to wait 10 hours to meet Justin Bieber, they can keep an eye on Justin Bieber while we’re on Rihanna. And if Justin Bieber happens to come out, they get their photos, and if they’re there, they’ll take video for us. Because we told them where Justin Bieber was to begin with.”

Diggs and Peña also recognize the stars’ drivers. They can guess who is in the club or restaurant by the car outside.

‘Snapchat is a celebrity’s worst nightmare right now because I have an eye like a hawk’
 - Miles Diggs
Recently, a magazine writer tipped them off that Kourtney Kardashian’s baby daddy, Scott Disick, was in town, so the pair monitored his usual haunts.

But Diggs was actually expecting singer Demi Lovato to emerge from the Trump Soho when he saw Disick step out — with an 18-year-old model .

“Boom. He just walked out the front door with a girl. As soon as they saw me, they split apart,” recalled Diggs, whose “eyes lit up with dollar signs.”

“If you’re friends with somebody, you keep walking,” Diggs said. “If you’re doing something with somebody, you split apart. It’s like an automatic guilt trip. So as soon as you see that, you know. They’re telling me that they’re sleeping together.”

Diggs and Peña followed the coy couple for five hours, waiting until Disick no longer suspected paps on their trail.

Finally, around midnight, they spotted the pair leaning on each other and play-fighting in a bar. They took their shots through their car window.

And they made sure not to create a stir, fearing it would bring attention to the couple — and spoil their exclusive.

“When you have something of that much value, after a while, it’s not about shooting more . . . it’s about protecting them from being seen,” Diggs said.

“Everybody with one of these is the enemy,” he said, holding up his iPhone. “I could shoot, with this huge camera right here, a crazy exclusive. A lady walks by — ‘Oh, this is for my grandson’ — takes a picture with her flip phone. We’re screwed.”

So Diggs and Peña waited ­until the next morning to send their work in to Splash News, their agents. That photo earned them $10,000 in the first day.

A picture’s worth
Sometimes finding celebrities is sheer luck.


Miles Diggs (left) and Cesar Peña catching up with Rihanna.
On a recent Wednesday, the pair was driving around in their tinted-windowed Chevrolet SUV when they spotted actor Bradley Cooper and his model girlfriend, Irina Shayk, walking down the street. The couple hadn’t been photographed together in months.

Diggs jumped out to follow on foot, trying to get ahead of them so he could photograph their faces.

“I got one or two frames, and they were off running,” he said.

It was a bizarre game of hide-and-seek, with the lovers trying to outrun the pap and ducking behind cars, and Diggs doing the same so they’d think he left.

Exclusives earn the most cash. One of the final video interviews with actor Philip Seymour Hoffman earned $6,000 to $7,000 for them after his death. Footage of Kristen Stewart and Robert Pattinson kissing in a car brought in $20,000. A recent exclusive of Rihanna in a Chelsea art gallery with artist Mr. Brainwash brought in $5,000.

Their biggest “get” ever was a photo and video of Beyoncé, Jay Z and Solange emerging from The Standard hotel in 2014 — right after Solange and the rapper’s elevator brawl.

After identifying Jay Z’s car by its driver, Diggs and Peña had set themselves up behind it to get the best angle. As a result, they were the only fotogs out of the dozens there to get a clear shot of all three celebs as they walked out the door.

Once leaked video of the brawl made news, Diggs’ and Peña’s shots were in high demand. Within a day, the footage brought in $35,000. Over a year, it has netted them $100,000.

What would be the greatest celebrity shot ever?

Easy: Brad Pitt and Jennifer Aniston having coffee together. They haven’t been seen together since their 2005 divorce.

“That’s like $500,000!” Diggs marveled.

“At least!” Pena laughed. “That’s the dream of all times.”
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By Amber JamiesonNovember 8, 2015 | 6:03am
https://nypost.com/2015/11/08/paparazzi-reveal-secrets-of-tracking-stars-on-social-media/

Big data and sentiment analysis: A comprehensive and systematic literature review

 

Abstract

Sentiment analysis can extract information from many text sources such as reviews, news, and blogs; then it classifies them based on their polarity. 

Moreover, big data is produced via mobile networks and social media. 

Applications of sentiment analysis on big data are used as a way of classifying the opinions into diverse sentiment. 

Accordingly, performing sentiment analysis on big data can be helpful for a business to take useful commercial insights from text-oriented content. 

However, there are very few comprehensive investigations and profound argument in this context. 

The goal of this paper is to provide a comprehensive and systematic investigation of the state-of-the-art techniques and highlight the directions for future research. 

In this paper, we used systematic literature review method and in the first step, we obtained 15 351 articles; then, based on different filters, 48 related articles were attained. 

We have selected 23 articles based on the year of publication, the relevance of the journal, the completeness of the text, the nonrepeatability of the title, and the page number. 

Also, we have categorized big data and sentiment analysis into two classifications: centralized and distributed platforms. 

Furthermore, the disadvantages and advantages of the investigated techniques are studied and their key issues are emphasized. 

Consequently, this study shows that a better analysis of textual big data in terms of sentiment increases efficiency, flexibility, and intelligence. 

By providing comparative information and analyzing the current developments in this area, this paper will directly support academics and practicing professionals for better handling of big data in the field of sentiment analysis. 

This study sheds some new light on using sentiment analysis and big data for public opinion estimation and prediction.

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https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.5671

Sentiment Analysis of Textual Content in Social Networks From Hand-Crafted to Deep Learning-Based Models [Problem Statement]

 

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...

 Most of the existing systems on sentiment analysis rely heavily on a rich set of sentiment resources (such as text corpora with manually annotated sentiment polarity, sentiment lexicons, and word embeddings). However, the distribution of sentiment resources is very imbalanced among languages. Thus, building a sentiment analysis system in low-resource languages requires tremendous human effort to construct such resources, which is a time-consuming and expensive task.

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Although opinions are the most common shared content in online social networks and forums, people also tend to share their emotions which are the keys to their feelings and thoughts. Emotion analysis is the task of determining the attitude towards a target or topic. The attitude can be the polarity (positive or negative), or an emotional state such as joy, anger or sadness [101–103].

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1.2 Objectives

In this thesis, we aim to develop methods to automatically analyse textual content shared on social networks and identify people’ opinions, emotions and feelings at different level of analysis and in different languages. As such, we introduce the following set of goals:

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• To develop efficient sentiment analysis systems based on new features that can be used with traditional Machine Learning (Machine Learning) methods. Towards this objective, we aim to use pre-built resources such as sentiment lexicons and word embeddings to extract new features.

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• To move sentiment analysis beyond sentence-level and polarity-based analysis. Towards this objective, we are interested in:

– Proposing ensemble systems that combine classical Machine Learning models with Deep Learning to analyse emotions expressed on Twitter.

– Developing a Deep Learning based model to solve the problem of multi-label emotions classification.

– Utilising Deep Learning based models to analyse opinions at the aspect level.

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• To move sentiment analysis beyond a single language.

We aim to utilise the concept of transfer learning to develop a system that can transfer sentiment knowledge from high resources languages to low resources languages. The final goal of this objective is to obtain a universal sentiment analysis system that works with low resource languages and does not require machine translation.

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• To test the developed systems on real cases of analysis

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• The idea of this objective is to test our developed systems in real applications and different domains. Hence, towards this objective, we define the following sub-goals:

– To collect tweets of local people, visitors and official brand destination offices from different tourist destinations and analyse the opinions shared in these tweets.

– To combine the aspect-based sentiment analysis with multi-criteria decision aid systems to improve the decision-making process.

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https://deim.urv.cat/~itaka/itaka2/PDF/acabats/PhD_Thesis/TESI_Mohammed_Jabreel.pdf

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Sentiment and position-taking analysis of parliamentary debates: A systematic literature review

 

Abstract

Parliamentary and legislative debate transcripts provide access to information concerning the opinions, positions and policy preferences of elected politicians. 

They attract attention from researchers from a wide variety of backgrounds, from political and social sciences to computer science. 

As a result, the problem of automatic sentiment and position-taking analysis has been tackled from different perspectives, using varying approaches and methods, and with relatively little collaboration or cross-pollination of ideas. 

The existing research is scattered across publications from various fields and venues. 

In this article we present the results of a systematic literature review of 61 studies, all of which address the automatic analysis of the sentiment and opinions expressed and positions taken by speakers in parliamentary (and other legislative) debates. 

In this review, we discuss the available research with regard to the aims and objectives of the researchers who work on these problems, the automatic analysis tasks they undertake, and the approaches and methods they use. 

We conclude by summarizing their findings, discussing the challenges of applying computational analysis to parliamentary debates, and suggesting possible avenues for further research.

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https://link.springer.com/article/10.1007%2Fs42001-019-00060-w

Machine learning algorithms and techniques for sentiment analysis in scientific paper reviews: A systematic literature review

Abstract

Sentiment analysis also referred to as opinion mining, is an automated process for identifying and classifying subjective information such as sentiments from a piece of text usually comments and reviews.

 Supported by machine learning algorithms, it is possible to identify positive, neutral or negative opinions, being possible to rank or classify them in order to reach some kind of conclusion or obtain any type of information. 

Thus, this paper aims to perform a systematic literature review in order to report the state-of-the-art of machine learning techniques for sentiment analysis applied to texts of reviews, comments and evaluations of scientific papers.

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https://repositorium.sdum.uminho.pt/handle/1822/65115

Sentiment Analysis in Scandinavian Languages: Systematic Review and Evaluation

 

Abstract

Natural Language Processing has seen a tremendous boost in popularity following the widespread use of the World Wide Web, and emergence of machine learning tools. 

The specific problem of sentiment analysis has become a popular topic with the availability of user generated content, from micro-blogs and the likes. 

But these data dependent problems have seen a larger jump in popularity in the international field, compared to low-resource languages, due to the availability of language specific data. 

This thesis seeks to delve into the problem of sentiment analysis research within some of these low-resource languages, specifically those of mainland Scandinavia, which are closely related languages. 

We perform a literature review to uncover popular research topics within this language specific field, and seek to find practical and theoretical work as well as resources within this field. 

Furthermore we perform experiments adapting international tools for these low-resource languages, and compare our results to that of the research, in order to further contribute to the language specific research field

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