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Visualizing Hot Topics Using Python To Analyze News Sitemaps

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Visualizing Hot Topics Using Python To Analyze News Sitemaps

News sitemaps use different and unique sitemap protocols to provide more information for the news search engines.

A news sitemap contains the news published in the last 48 hours.

News sitemap tags include the news publication’s title, language, name, genre, publication date, keywords, and even stock tickers.

How can you use these sitemaps to your advantage for content research and competitive analysis?

In this Python tutorial, you’ll learn a 10-step process for analyzing news sitemaps and visualizing topical trends discovered therein.

Housekeeping Notes To Get Us Started

This tutorial was written during Russia’s invasion of Ukraine.

Using machine learning, we can even label news sources and articles according to which news source is “objective” and which news source is “sarcastic.”

But to keep things simple, we will focus on topics with frequency analysis.

We will use more than 10 global news sources across the U.S. and U.K.

Note: We would like to include Russian news sources, but they do not have a proper news sitemap. Even if they had, they block the external requests.

Comparing the word occurrence of “invasion” and “liberation” from Western and Eastern news sources shows the benefit of distributional frequency text analysis methods.

What You Need To Analyze News Content With Python

The related Python libraries for auditing a news sitemap to understand the news source’s content strategy are listed below:

  • Advertools.
  • Pandas.
  • Plotly Express, Subplots, and Graph Objects.
  • Re (Regex).
  • String.
  • NLTK (Corpus, Stopwords, Ngrams).
  • Unicodedata.
  • Matplotlib.
  • Basic Python Syntax Understanding.

10 Steps For News Sitemap Analysis With Python

All set up? Let’s get to it.

1. Take The News URLs From News Sitemap

We chose the “The Guardian,” “New York Times,” “Washington Post,” “Daily Mail,” “Sky News,” “BBC,” and “CNN” to examine the News URLs from the News Sitemaps.

df_guardian = adv.sitemap_to_df("http://www.theguardian.com/sitemaps/news.xml")
df_nyt = adv.sitemap_to_df("https://www.nytimes.com/sitemaps/new/news.xml.gz")
df_wp = adv.sitemap_to_df("https://www.washingtonpost.com/arcio/news-sitemap/")
df_bbc = adv.sitemap_to_df("https://www.bbc.com/sitemaps/https-index-com-news.xml")
df_dailymail = adv.sitemap_to_df("https://www.dailymail.co.uk/google-news-sitemap.xml")
df_skynews = adv.sitemap_to_df("https://news.sky.com/sitemap-index.xml")
df_cnn = adv.sitemap_to_df("https://edition.cnn.com/sitemaps/cnn/news.xml")

2. Examine An Example News Sitemap With Python

I have used BBC as an example to demonstrate what we just extracted from these news sitemaps.

df_bbc
News Sitemap Data Frame View

The BBC Sitemap has the columns below.

df_bbc.columns
News Sitemap TagsNews Sitemap Tags as data frame columns

The general data structures of these columns are below.

df_bbc.info()
News Sitemap as a DataframeNews Sitemap Columns and Data types

The BBC doesn’t use the “news_publication” column and others.

3. Find The Most Used Words In URLs From News Publications

To see the most used words in the news sites’ URLs, we need to use “str,” “explode”, and “split” methods.

df_dailymail["loc"].str.split("/").str[5].str.split("-").explode().value_counts().to_frame()
loc
article
176
Russian
50
Ukraine
50
says
38
reveals
38
...
...
readers
1
Red
1
Cross
1
provide
1
weekend.html
1
5445 rows × 1 column

We see that for the “Daily Mail,” “Russia and Ukraine” are the main topic.

4. Find The Most Used Language In News Publications

The URL structure or the “language” section of the news publication can be used to see the most used languages in news publications.

In this sample, we used “BBC” to see their language prioritization.

df_bbc["publication_language"].head(20).value_counts().to_frame()
publication_language
en
698
fa
52
sr
52
ar
47
mr
43
hi
43
gu
41
ur
35
pt
33
te
31
ta
31
cy
30
ha
29
tr
28
es
25
sw
22
cpe
22
ne
21
pa
21
yo
20
20 rows × 1 column

To reach out to the Russian population via Google News, every western news source should use the Russian language.

Some international news institutions started to perform this perspective.

If you are a news SEO, it’s helpful to watch Russian language publications from competitors to distribute the objective news to Russia and compete within the news industry.

5. Audit The News Titles For Frequency Of Words

We used BBC to see the “news titles” and which words are more frequent.

df_bbc["news_title"].str.split(" ").explode().value_counts().to_frame()
news_title
to
232
in
181
-
141
of
140
for
138
...
...
ፊልም
1
ብላክ
1
ባንኪ
1
ጕሒላ
1
niile
1
11916 rows × 1 columns

The problem here is that we have “every type of word in the news titles,” such as “contextless stop words.”

We need to clean these types of non-categorical terms to understand their focus better.

from nltk.corpus import stopwords
stop = stopwords.words('english')
df_bbc_news_title_most_used_words = df_bbc["news_title"].str.split(" ").explode().value_counts().to_frame()
pat = r'b(?:{})b'.format('|'.join(stop))
df_bbc_news_title_most_used_words.reset_index(drop=True, inplace=True)
df_bbc_news_title_most_used_words["without_stop_words"] = df_bbc_news_title_most_used_words["words"].str.replace(pat,"")
df_bbc_news_title_most_used_words.drop(df_bbc_news_title_most_used_words.loc[df_bbc_news_title_most_used_words["without_stop_words"]==""].index, inplace=True)
df_bbc_news_title_most_used_words
Removing Stop Words from Text AnalysisThe “without_stop_words” column involves the cleaned text values.

We have removed most of the stop words with the help of the “regex” and “replace” method of Pandas.

The second concern is removing the “punctuations.”

For that, we will use the “string” module of Python.

import string
df_bbc_news_title_most_used_words["without_stop_word_and_punctation"] = df_bbc_news_title_most_used_words['without_stop_words'].str.replace('[{}]'.format(string.punctuation), '')
df_bbc_news_title_most_used_words.drop(df_bbc_news_title_most_used_words.loc[df_bbc_news_title_most_used_words["without_stop_word_and_punctation"]==""].index, inplace=True)
df_bbc_news_title_most_used_words.drop(["without_stop_words", "words"], axis=1, inplace=True)
df_bbc_news_title_most_used_words
news_title
without_stop_word_and_punctation
Ukraine
110
Ukraine
v
83
v
de
61
de
Ukraine:
60
Ukraine
da
51
da
...
...
...
ፊልም
1
ፊልም
ብላክ
1
ብላክ
ባንኪ
1
ባንኪ
ጕሒላ
1
ጕሒላ
niile
1
niile
11767 rows × 2 columns

Or, use “df_bbc_news_title_most_used_words[“news_title”].to_frame()” to take a more clear picture of data.

news_title
Ukraine
110
v
83
de
61
Ukraine:
60
da
51
...
...
ፊልም
1
ብላክ
1
ባንኪ
1
ጕሒላ
1
niile
1
11767 rows × 1 columns

We see 11,767 unique words in the URLs of the BBC, and Ukraine is the most popular, with 110 occurrences.

There are different Ukraine-related phrases from the data frame, such as “Ukraine:.”

The “NLTK Tokenize” can be used to unite these types of different variations.

The next section will use a different method to unite them.

Note: If you want to make things easier, use Advertools as below.

adv.word_frequency(df_bbc["news_title"],phrase_len=2, rm_words=adv.stopwords.keys())

The result is below.

Text Analysis and WordText Analysis with Advertools

“adv.word_frequency” has the attributes “phrase_len” and “rm_words” to determine the length of the phrase occurrence and remove the stop words.

You may tell me, why didn’t I use it in the first place?

I wanted to show you an educational example with “regex, NLTK, and the string” so that you can understand what’s happening behind the scenes.

6. Visualize The Most Used Words In News Titles

To visualize the most used words in the news titles, you can use the code block below.

df_bbc_news_title_most_used_words["news_title"] = df_bbc_news_title_most_used_words["news_title"].astype(int)
df_bbc_news_title_most_used_words["without_stop_word_and_punctation"] = df_bbc_news_title_most_used_words["without_stop_word_and_punctation"].astype(str)
df_bbc_news_title_most_used_words.index = df_bbc_news_title_most_used_words["without_stop_word_and_punctation"]
df_bbc_news_title_most_used_words["news_title"].head(20).plot(title="The Most Used Words in BBC News Titles")
News Sitemap Python AnalysisNews NGrams Visualization

You realize that there is a “broken line.”

Do you remember the “Ukraine” and “Ukraine:” in the data frame?

When we remove the “punctuation,” the second and first values become the same.

That’s why the line graph says that Ukraine appeared 60 times and 110 times separately.

To prevent such a data discrepancy, use the code block below.

df_bbc_news_title_most_used_words_1 = df_bbc_news_title_most_used_words.drop_duplicates().groupby('without_stop_word_and_punctation', sort=False, as_index=True).sum()
df_bbc_news_title_most_used_words_1
news_title
without_stop_word_and_punctation
Ukraine
175
v
83
de
61
da
51
и
41
...
...
ፊልም
1
ብላክ
1
ባንኪ
1
ጕሒላ
1
niile
1
11109 rows × 1 columns

The duplicated rows are dropped, and their values are summed together.

Now, let’s visualize it again.

7. Extract Most Popular N-Grams From News Titles

Extracting n-grams from the news titles or normalizing the URL words and forming n-grams for understanding the overall topicality is useful to understand which news publication approaches which topic. Here’s how.

import nltk
import unicodedata
import re
def text_clean(content):
  lemmetizer = nltk.stem.WordNetLemmatizer()

  stopwords = nltk.corpus.stopwords.words('english')

  content = (unicodedata.normalize('NFKD', content)

    .encode('ascii', 'ignore')

    .decode('utf-8', 'ignore')

    .lower())

  words = re.sub(r'[^ws]', '', content).split()

  return [lemmetizer.lemmatize(word) for word in words if word not in stopwords]
raw_words = text_clean(''.join(str(df_bbc['news_title'].tolist())))
raw_words[:10]
OUTPUT>>>
['oneminute', 'world', 'news', 'best', 'generation', 'make', 'agyarkos', 'dream', 'fight', 'card']

The output shows we have “lemmatized” all the words in the news titles and put them in a list.

The list comprehension provides a quick shortcut for filtering every stop word easily.

Using “nltk.corpus.stopwords.words(“english”)” provides all the stop words in English.

But you can add extra stop words to the list to expand the exclusion of words.

The “unicodedata” is to canonicalize the characters.

The characters that we see are actually Unicode bytes like “U+2160 ROMAN NUMERAL ONE” and the Roman Character “U+0049 LATIN CAPITAL LETTER I” are actually the same.

The “unicodedata.normalize” distinguishes the character differences so that the lemmatizer can differentiate the different words with similar characters from each other.

pd.set_option("display.max_colwidth",90)

bbc_bigrams = (pd.Series(ngrams(words, n = 2)).value_counts())[:15].sort_values(ascending=False).to_frame()

bbc_trigrams = (pd.Series(ngrams(words, n = 3)).value_counts())[:15].sort_values(ascending=False).to_frame()

Below, you will see the most popular “n-grams” from BBC News.

Bigrams of BBCNGrams Dataframe from BBC

To simply visualize the most popular n-grams of a news source, use the code block below.

bbc_bigrams.plot.barh(color="red", width=.8,figsize=(10 , 7))

“Ukraine, war” is the trending news.

You can also filter the n-grams for “Ukraine” and create an “entity-attribute” pair.

News Sitemap NGramsNews Sitemap NGrams from BBC

Crawling these URLs and recognizing the “person type entities” can give you an idea about how BBC approaches newsworthy situations.

But it is beyond “news sitemaps.” Thus, it is for another day.

To visualize the popular n-grams from news source’s sitemaps, you can create a custom python function as below.

def ngram_visualize(dataframe:pd.DataFrame, color:str="blue") -> pd.DataFrame.plot:

     dataframe.plot.barh(color=color, width=.8,figsize=(10 ,7))
ngram_visualize(ngram_extractor(df_dailymail))

The result is below.

N-Gram VisualizationNews Sitemap Trigram Visualization

To make it interactive, add an extra parameter as below.

def ngram_visualize(dataframe:pd.DataFrame, backend:str, color:str="blue", ) -> pd.DataFrame.plot:

     if backend=="plotly":

          pd.options.plotting.backend=backend

          return dataframe.plot.bar()

     else:

          return dataframe.plot.barh(color=color, width=.8,figsize=(10 ,7))
ngram_visualize(ngram_extractor(df_dailymail), backend="plotly")

As a quick example, check below.

8. Create Your Own Custom Functions To Analyze The News Source Sitemaps

When you audit news sitemaps repeatedly, there will be a need for a small Python package.

Below, you can find four different quick Python function chain that uses every previous function as a callback.

To clean a textual content item, use the function below.

def text_clean(content):

  lemmetizer = nltk.stem.WordNetLemmatizer()

  stopwords = nltk.corpus.stopwords.words('english')

  content = (unicodedata.normalize('NFKD', content)

    .encode('ascii', 'ignore')

    .decode('utf-8', 'ignore')

    .lower())

  words = re.sub(r'[^ws]', '', content).split()

  return [lemmetizer.lemmatize(word) for word in words if word not in stopwords]

To extract the n-grams from a specific news website’s sitemap’s news titles, use the function below.

def ngram_extractor(dataframe:pd.DataFrame|pd.Series):

     if "news_title" in dataframe.columns:

          return dataframe_ngram_extractor(dataframe,  ngram=3, first=10)

Use the function below to turn the extracted n-grams into a data frame.

def dataframe_ngram_extractor(dataframe:pd.DataFrame|pd.Series, ngram:int, first:int):

     raw_words = text_clean(''.join(str(dataframe['news_title'].tolist())))

     return (pd.Series(ngrams(raw_words, n = ngram)).value_counts())[:first].sort_values(ascending=False).to_frame()

To extract multiple news websites’ sitemaps, use the function below.

def ngram_df_constructor(df_1:pd.DataFrame, df_2:pd.DataFrame):

  df_1_bigrams = dataframe_ngram_extractor(df_1, ngram=2, first=500)

  df_1_trigrams = dataframe_ngram_extractor(df_1, ngram=3, first=500)

  df_2_bigrams = dataframe_ngram_extractor(df_2, ngram=2, first=500)

  df_2_trigrams = dataframe_ngram_extractor(df_2, ngram=3, first=500)

  ngrams_df = {

  "df_1_bigrams":df_1_bigrams.index,

  "df_1_trigrams": df_1_trigrams.index,

  "df_2_bigrams":df_2_bigrams.index,

  "df_2_trigrams": df_2_trigrams.index,

  }

  dict_df = (pd.DataFrame({ key:pd.Series(value) for key, value in ngrams_df.items() }).reset_index(drop=True)

  .rename(columns={"df_1_bigrams":adv.url_to_df(df_1["loc"])["netloc"][1].split("www.")[1].split(".")[0] + "_bigrams",

                    "df_1_trigrams":adv.url_to_df(df_1["loc"])["netloc"][1].split("www.")[1].split(".")[0] + "_trigrams",

                    "df_2_bigrams": adv.url_to_df(df_2["loc"])["netloc"][1].split("www.")[1].split(".")[0] + "_bigrams",

                    "df_2_trigrams": adv.url_to_df(df_2["loc"])["netloc"][1].split("www.")[1].split(".")[0] + "_trigrams"}))

  return dict_df

Below, you can see an example use case.

ngram_df_constructor(df_bbc, df_guardian)
Ngram PopularityPopular Ngram Comparison to see the news websites’ focus.

Only with these nested four custom python functions can you do the things below.

  • Easily, you can visualize these n-grams and the news website counts to check.
  • You can see the focus of the news websites for the same topic or different topics.
  • You can compare their wording or the vocabulary for the same topics.
  • You can see how many different sub-topics from the same topics or entities are processed in a comparative way.

I didn’t put the numbers for the frequencies of the n-grams.

But, the first ranked ones are the most popular ones from that specific news source.

To examine the next 500 rows, click here.

9. Extract The Most Used News Keywords From News Sitemaps

When it comes to news keywords, they are surprisingly still active on Google.

For example, Microsoft Bing and Google do not think that “meta keywords” are a useful signal anymore, unlike Yandex.

But, news keywords from the news sitemaps are still used.

Among all these news sources, only The Guardian uses the news keywords.

And understanding how they use news keywords to provide relevance is useful.

df_guardian["news_keywords"].str.split().explode().value_counts().to_frame().rename(columns={"news_keywords":"news_keyword_occurence"})

You can see the most used words in the news keywords for The Guardian.

news_keyword_occurence
news,
250
World
142
and
142
Ukraine,
127
UK
116
...
...
Cumberbatch,
1
Dune
1
Saracens
1
Pearson,
1
Thailand
1
1409 rows × 1 column

The visualization is below.

(df_guardian["news_keywords"].str.split().explode().value_counts()

.to_frame().rename(columns={"news_keywords":"news_keyword_occurence"})

.head(25).plot.barh(figsize=(10,8),

title="The Guardian Most Used Words in News Keywords", xlabel="News Keywords",

legend=False, ylabel="Count of News Keyword"))

Most Popular Words in News KeywordsMost Popular Words in News Keywords

The “,” at the end of the news keywords represent whether it is a separate value or part of another.
I suggest you not remove the “punctuations” or “stop words” from news keywords so that you can see their news keyword usage style better.

For a different analysis, you can use “,” as a separator.

df_guardian["news_keywords"].str.split(",").explode().value_counts().to_frame().rename(columns={"news_keywords":"news_keyword_occurence"})

The result difference is below.

news_keyword_occurence
World news
134
Europe
116
UK news
111
Sport
109
Russia
90
...
...
Women's shoes
1
Men's shoes
1
Body image
1
Kae Tempest
1
Thailand
1
1080 rows × 1 column

Focus on the “split(“,”).”

(df_guardian["news_keywords"].str.split(",").explode().value_counts()

.to_frame().rename(columns={"news_keywords":"news_keyword_occurence"})

.head(25).plot.barh(figsize=(10,8),

title="The Guardian Most Used Words in News Keywords", xlabel="News Keywords",

legend=False, ylabel="Count of News Keyword"))

You can see the result difference for visualization below.

Most Popular Keywords from News SitemapsMost Popular Keywords from News Sitemaps

From “Chelsea” to “Vladamir Putin” or “Ukraine War” and “Roman Abramovich,” most of these phrases align with the early days of Russia’s Invasion of Ukraine.

Use the code block below to visualize two different news website sitemaps’ news keywords interactively.

df_1 = df_guardian["news_keywords"].str.split(",").explode().value_counts().to_frame().rename(columns={"news_keywords":"news_keyword_occurence"})

df_2 = df_nyt["news_keywords"].str.split(",").explode().value_counts().to_frame().rename(columns={"news_keywords":"news_keyword_occurence"})

fig = make_subplots(rows = 1, cols = 2)

fig.add_trace(

     go.Bar(y = df_1["news_keyword_occurence"][:6].index, x = df_1["news_keyword_occurence"], orientation="h", name="The Guardian News Keywords"), row=1, col=2

)

fig.add_trace(

     go.Bar(y = df_2["news_keyword_occurence"][:6].index, x = df_2["news_keyword_occurence"], orientation="h", name="New York Times News Keywords"), row=1, col=1

)

fig.update_layout(height = 800, width = 1200, title_text="Side by Side Popular News Keywords")

fig.show()

fig.write_html("news_keywords.html")

You can see the result below.

To interact with the live chart, click here.

In the next section, you will find two different subplot samples to compare the n-grams of the news websites.

10. Create Subplots For Comparing News Sources

Use the code block below to put the news sources’ most popular n-grams from the news titles to a sub-plot.

import matplotlib.pyplot as plt

import pandas as pd

df1 = ngram_extractor(df_bbc)

df2 = ngram_extractor(df_skynews)

df3 = ngram_extractor(df_dailymail)

df4 = ngram_extractor(df_guardian)

df5 = ngram_extractor(df_nyt)

df6 = ngram_extractor(df_cnn)

nrow=3

ncol=2

df_list = [df1 ,df2, df3, df4, df5, df6] #df6

titles = ["BBC News Trigrams", "Skynews Trigrams", "Dailymail Trigrams", "The Guardian Trigrams", "New York Times Trigrams", "CNN News Ngrams"]

fig, axes = plt.subplots(nrow, ncol, figsize=(25,32))

count=0

i = 0

for r in range(nrow):

    for c in range(ncol):

        (df_list[count].plot.barh(ax = axes[r,c],

        figsize = (40, 28),

        title = titles[i],

        fontsize = 10,

        legend = False,

        xlabel = "Trigrams",

        ylabel = "Count"))        

        count+=1

        i += 1

You can see the result below.

News Source NGramsMost Popular NGrams from News Sources

The example data visualization above is entirely static and doesn’t provide any interactivity.

Lately, Elias Dabbas, creator of Advertools, has shared a new script to take the article count, n-grams, and their counts from the news sources.

Check here for a better, more detailed, and interactive data dashboard.

The example above is from Elias Dabbas, and he demonstrates how to take the total article count, most frequent words, and n-grams from news websites in an interactive way.

Final Thoughts On News Sitemap Analysis With Python

This tutorial was designed to provide an educational Python coding session to take the keywords, n-grams, phrase patterns, languages, and other kinds of SEO-related information from news websites.

News SEO heavily relies on quick reflexes and always-on article creation.

Tracking your competitors’ angles and methods for covering a topic shows how the competitors have quick reflexes for the search trends.

Creating a Google Trends Dashboard and News Source Ngram Tracker for a comparative and complementary news SEO analysis would be better.

In this article, from time to time, I have put custom functions or advanced for loops, and sometimes, I have kept things simple.

Beginners to advanced Python practitioners can benefit from it to improve their tracking, reporting, and analyzing methodologies for news SEO and beyond.

More resources:


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Google Quietly Ends Covid-Era Rich Results

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Google Quietly Ends Covid-Era Rich Results

Google removed the Covid-era structured data associated with the Home Activities rich results that allowed online events to be surfaced in search since August 2020, publishing a mention of the removal in the search documentation changelog.

Home Activities Rich Results

The structured data for the Home Activities rich results allowed providers of online livestreams, pre-recorded events and online events to be findable in Google Search.

The original documentation has been completely removed from the Google Search Central webpages and now redirects to a changelog notation that explains that the Home Activity rich results is no longer available for display.

The original purpose was to allow people to discover things to do from home while in quarantine, particularly online classes and events. Google’s rich results surfaced details of how to watch, description of the activities and registration information.

Providers of online events were required to use Event or Video structured data. Publishers and businesses who have this kind of structured data should be aware that this kind of rich result is no longer surfaced but it’s not necessary to remove the structured data if it’s a burden, it’s not going to hurt anything to publish structured data that isn’t used for rich results.

The changelog for Google’s official documentation explains:

“Removing home activity documentation
What: Removed documentation on home activity structured data.

Why: The home activity feature no longer appears in Google Search results.”

Read more about Google’s Home Activities rich results:

Google Announces Home Activities Rich Results

Read the Wayback Machine’s archive of Google’s original announcement from 2020:

Home activities

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Google’s Gary Illyes: Lastmod Signal Is Binary

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Google's Gary Illyes: Lastmod Signal Is Binary

In a recent LinkedIn discussion, Gary Illyes, Analyst at Google, revealed that the search engine takes a binary approach when assessing a website’s lastmod signal from sitemaps.

The revelation came as Illyes encouraged website owners to upgrade to WordPress 6.5, which now natively supports the lastmod element in sitemaps.

When Mark Williams-Cook asked if Google has a “reputation system” to gauge how much to trust a site’s reported lastmod dates, Illyes stated, “It’s binary: we either trust it or we don’t.”

No Shades Of Gray For Lastmod

The lastmod tag indicates the date of the most recent significant update to a webpage, helping search engines prioritize crawling and indexing.

Illyes’ response suggests Google doesn’t factor in a website’s history or gradually build trust in the lastmod values being reported.

Google either accepts the lastmod dates provided in a site’s sitemap as accurate, or it disregards them.

This binary approach reinforces the need to implement the lastmod tag correctly and only specify dates when making meaningful changes.

Illyes commends the WordPress developer community for their work on version 6.5, which automatically populates the lastmod field without extra configuration.

Accurate Lastmod Essential For Crawl Prioritization

While convenient for WordPress users, the native lastmod support is only beneficial if Google trusts you’re using it correctly.

Inaccurate lastmod tags could lead to Google ignoring the signal when scheduling crawls.

With Illyes confirming Google’s stance, it shows there’s no room for error when using this tag.

Why SEJ Cares

Understanding how Google acts on lastmod can help ensure Google displays new publish dates in search results when you update your content.

It’s an all-or-nothing situation – if the dates are deemed untrustworthy, the signal could be disregarded sitewide.

With the information revealed by Illyes, you can ensure your implementation follows best practices to the letter.


Featured Image: Danishch/Shutterstock

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How to Persuade Your Boss to Send You to Ahrefs Evolve

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How to Persuade Your Boss to Send You to Ahrefs Evolve

There’s one thing standing between you and several days of SEO, socializing, and Singaporean sunshine: your boss (and their Q4 budget 😅).

But don’t worry—we’ve got your back. Here are 5 arguments (and an example message) you can use to persuade your boss to send you to Ahrefs Evolve.

About Ahrefs Evolve

  • 2 days in sunny Singapore (Oct 24–25)
  • 500 digital marketing enthusiasts
  • 18 top speakers from around the world

Learn more and buy tickets.

SEO is changing at a breakneck pace. Between AI Overviews, Google’s rolling update schedule, their huge API leak, and all the documents released during their antitrust trial, it’s hard to keep up. What works in SEO today?

You could watch a YouTube video or two, maybe even attend an hour-long webinar. Or, much more effective: you could spend two full days learning from a panel of 18 international SEO experts, discussing your takeaways live with other attendees.

How to Persuade Your Boss to Send You to AhrefsHow to Persuade Your Boss to Send You to Ahrefs
Evolve speakers from around the world.

Our world-class speakers are tackling the hardest problems and best opportunities in SEO today. The talk agenda covers topics like:

  • Responding to AI Overviews: Amanda King will teach you how to respond to AI Overviews, Google Gemini, and other AI search functions.
  • Surviving (and thriving) Google’s algo updates: Lily Ray will talk through Google’s recent updates, and share data-driven recommendations for what’s working in search today.
  • Planning for the future of SEO: Bernard Huang will talk through the failures of AI content and the path to better results.

(And attendees will get video recordings of each session, so you can share the knowledge with your teammates too.)

View the full talk agenda here.

There’s no substitute for meeting with influencers, peers, and partners in real life. 

Conferences create serendipity: chance encounters and conversations that can have a huge positive impact on you and your business. By way of example, these are some of the real benefits that have come my way from attending conferences:

  • Conversations that lead to new customers for our business,
  • Invitations to speak at events,
  • New business partnerships and co-marketing opportunities, and
  • Meeting people that we went on to hire.

There’s a “halo” effect that lingers long after the event is over: the people you meet will remember you for longer, think more highly of you, and be more likely to help you out, should you ask.

(And let’s not forget: there’s a lot of information, particularly in SEO, that only gets shared in person.)

The “international” part of Evolve matters too. Evolve is a different crowd to your local run-of-the-mill conference. It’s a chance to meet with people from markets you wouldn’t normally meet—from Australia to Indonesia and beyond.

How to Persuade Your Boss to Send You to AhrefsHow to Persuade Your Boss to Send You to Ahrefs
Evolve attendees by home country.

If you’re an Ahrefs customer (thank you!), you’ll learn tons of tips, tricks and workflow improvements from attending Evolve. You’ll have opportunities to:

  • Attend talks from the Ahrefs team, showcasing advanced features and strategies that you can use in your own business.
  • Pick our brains at the Ahrefs booth, where we’ll offer informal 1:1 coaching sessions and previews of up-coming releases (like our new content optimization tool 🤫).
  • Join dedicated Ahrefs training workshops, hosted by the Ahrefs team and Ahrefs power users (tickets for these workshops will sold separately).

As a manager myself, there are two questions I need answered when approving expenses:

  • Is this a reasonable cost?
  • Will we see a return on this investment?

To answer those questions: early bird tickets for Evolve start at $570. For context, “super early bird” tickets for MozCon (another popular SEO conference) this year were almost twice as much: $999.

There’s a lot included in the ticket price too:

  • World-class international speakers,
  • 5-star hotel venue,
  • 5-star hotel food (two tea breaks with snacks & lunch),
  • Networking afterparty, and
  • Full talk recordings to later share with your team.

SEO is a crucial growth channel for most businesses. If you can improve your company’s SEO performance after attending Evolve (and we think you will), you’ll very easily see a positive return on the investment.

Traveling to tropical Singapore (and eating tons of satay) is great for you, but it’s also great for your team. Attending Evolve is a chance to break with routine, reignite your passion for marketing, and come back to your job reinvigorated.

This would be true for any international conference, but it goes double for Singapore. It’s a truly unique place: an ultra-safe, high-tech city that brings together dozens of different cultures.

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Little India in Singapore

You’ll discover different beliefs, working practices, and ways of business—and if you’re anything like me, come back a richer, wiser person for the experience.

If you’re nervous about pitching your boss on attending Evolve, remember: the worst that can happen is a polite “not this time”, and you’ll find yourself in the same position you are now.

So here goes: take this message template, tweak it to your liking, and send it to your boss over email or Slack… and I’ll see you in Singapore 😉

Email template

Hi [your boss’ name],

Our SEO tool provider, Ahrefs, is holding an SEO and digital marketing conference in Singapore in October. I’d like to attend, and I think it’s in the company’s interest:

  • The talks will help us respond to all the changes happening in SEO today. I’m particularly interested in the talks about AI and recent Google updates. 
  • I can network with my peers. I can discover what’s working at other companies, and explore opportunities for partnerships and co-marketing.
  • I can learn how we can use Ahrefs better across the organization.
  • I’ll come back reinvigorated with new ideas and motivation, and I can share my top takeaways and talk recordings with my team after the event.

Early bird tickets are $570. Given how important SEO is to the growth of our business, I think we’ll easily see a return from the spend.

Can we set up time to chat in more detail? Thanks!

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