Several years ago, I embarked on a project to collect the headers of all the messages I could reach on NNTP, with the original intent of studying the progression of the most common news clients. More recently, I used this dataset to attempt to discover the prevalence of charsets in email messages. In doing so, I identified a critical problem with the dataset: since it only contains headers, there is very little scope for actually understanding the full, sad story of charsets. So I've decided to rectify this problem.

This time, I modified my data-collection scripts to make it much easier to mass-download NNTP messages. The first script effectively lists all the newsgroups, and then all the message IDs in those newsgroups, stuffing the results in a set to remove duplicates (cross-posts). The second script uses Python's nntplib package to attempt to download all of those messages. Of the 32,598,261 messages identified by the first set, I succeeded in obtaining 1,025,586 messages in full or in part. Some messages failed to download due to crashing nntplib (which appears to be unable to handle messages of unbounded length), and I suspect my newsserver connections may have just timed out in the middle of the download at times. Others failed due to expiring before I could download them. All in all, 19,288 messages were not downloaded.

Analysis of the contents of messages were hampered due to a strong desire to find techniques that could mangle messages as little as possible. Prior experience with Python's message-parsing libraries lend me to believe that they are rather poor at handling some of the crap that comes into existence, and the errors in nntplib suggest they haven't fixed them yet. The only message parsing framework I truly trust to give me the level of finess is the JSMime that I'm writing, but that happens to be in the wrong language for this project. After reading some blog posts of Jeffrey Stedfast, though, I decided I would give GMime a try instead of trying to rewrite ad-hoc MIME parser #N.

Ultimately, I wrote a program to investigate the following questions on how messages operate in practice:

• What charsets are used in practice? How are these charsets named?
• For undeclared charsets, what are the correct charsets?
• For charsets unknown to a decoder, how often would ASCII suffice?
• What charsets are used in RFC 2047 encoded words?
• How prevalent are malformed RFC 2047 encoded words?
• When HTML and MIME are mixed, who wins?
• What is the state of 8-bit headers?

While those were the questions I seeked the answers to originally, I did come up with others as I worked on my tool, some in part due to what information I was basically already collecting. The tool I wrote primarily uses GMime to convert the body parts to 8-bit text (no charset conversion), as well as parse the Content-Type headers, which are really annoying to do without writing a full parser. I used ICU to handle charset conversion and detection. RFC 2047 decoding is done largely by hand since I needed very specific information that I couldn't convince GMime to give me. All code that I used is available upon request; the exact dataset is harder to transport, given that it is some 5.6GiB of data.

Other than GMime being built on GObject and exposing a C API, I can't complain much, although I didn't try to use it to do magic. Then again, in my experience (and as this post will probably convince you as well), you really want your MIME library to do charset magic for you, so in doing well for my needs, it's actually not doing well for a larger audience. ICU's C API similarly makes me want to complain. However, I'm now very suspect of the quality of its charset detection code, which is the main reason I used it. Trying to figure out how to get it to handle the charset decoding errors also proved far more annoying than it really should.

Some final background regards the biases I expect to crop up in the dataset. As the approximately 1 million messages were drawn from the python set iterator, I suspect that there's no systematic bias towards or away from specific groups, excepting that the ~11K messages found in the eternal-september.* hierarchy are completely represented. The newsserver I used, Eternal September, has a respectably large set of newsgroups, although it is likely to be biased towards European languages and under-representing East Asians. The less well-connected South America, Africa, or central Asia are going to be almost completely unrepresented. The download process will be biased away towards particularly heinous messages (such as exceedingly long lines), since nntplib itself is failing.

This being news messages, I also expect that use of 8-bit will be far more common than would be the case in regular mail messages. On a related note, the use of 8-bit in headers would be commensurately elevated compared to normal email. What would be far less common is HTML. I also expect that undeclared charsets may be slightly higher.

Charsets

Charset data is mostly collected on the basis of individual body parts within body messages; some messages have more than one. Interestingly enough, the 1,025,587 messages yielded 1,016,765 body parts with some text data, which indicates that either the messages on the server had only headers in the first place or the download process somehow managed to only grab the headers. There were also 393 messages that I identified having parts with different charsets, which only further illustrates how annoying charsets are in messages.

The aliases in charsets are mostly uninteresting in variance, except for the various labels used for US-ASCII (us - ascii, 646, and ANSI_X3.4-1968 are the less-well-known aliases), as well as the list of charsets whose names ICU was incapable of recognizing, given below. Unknown charsets are treated as equivalent to undeclared charsets in further processing, as there were too few to merit separate handling (45 in all).

• x-windows-949
• isolatin
• ISO-IR-111
• Default
• ISO-8859-1 format=flowed
• X-UNKNOWN
• x-user-defined
• windows-874
• 3D"us-ascii"
• x-koi8-ru
• windows-1252 (fuer gute Newsreader)
• LATIN-1#2 iso-8859-1

For the next step, I used ICU to attempt to detect the actual charset of the body parts. ICU's charset detector doesn't support the full gamut of charsets, though, so charset names not claimed to be detected were instead processed by checking if they decoded without error. Before using this detection, I detect if the text is pure ASCII (excluding control characters, to enable charsets like ISO-2022-JP, and +, if the charset we're trying to check is UTF-7). ICU has a mode which ignores all text in things that look like HTML tags, and this mode is set for all HTML body parts.

I don't quite believe ICU's charset detection results, so I've collapsed the results into a simpler table to capture the most salient feature. The correct column indicates the cases where the detected result was the declared charset. The ASCII column captures the fraction which were pure ASCII. The UTF-8 column indicates if ICU reported that the text was UTF-8 (it always seems to try this first). The Wrong C1 column refers to an ISO-8859-1 text being detected as windows-1252 or vice versa, which is set by ICU if it sees or doesn't see an octet in the appropriate range. The other column refers to all other cases, including invalid cases for charsets not supported by ICU.

Declared	Correct	ASCII	UTF-8	Wrong C1	Other	Total
ISO-8859-1	230,526	225,667	883	8,119	1,035	466,230
Undeclared		148,054	1,116		37,626	186,796
UTF-8	75,674	37,600			1,551	114,825
US-ASCII		98,238	0		304	98,542
ISO-8859-15	67,529	18,527			0	86,056
windows-1252	21,414	4,370	154	3,319	130	29,387
ISO-8859-2	18,647	2,138	70	71	2,319	23,245
KOI8-R	4,616	424	2		1,112	6,154
GB2312	1,307	59	0		112	1,478
Big5	622	608	0		174	1,404
windows-1256	343	10	0		45	398
IBM437	84	257			0	341
ISO-8859-13	311	6			0	317
windows-1251	131	97	1		61	290
windows-1250	69	69	0	14	101	253
ISO-8859-7	26	26	0	0	131	183
ISO-8859-9	127	11	0	0	17	155
ISO-2022-JP	76	69	0		3	148
macintosh	67	57			0	124
ISO-8859-16	0	15			101	116
UTF-7	51	4			0	55
x-mac-croatian	0	13			25	38
KOI8-U	28	2			0	30
windows-1255	0	18	0	0	6	24
ISO-8859-4	23	0			0	23
EUC-KR	0	3	0		16	19
ISO-8859-14	14	4			0	18
GB18030	14	3	0		0	17
ISO-8859-8	0	0	0	0	16	16
TIS-620	15	0			0	15
Shift_JIS	8	4	0		1	13
ISO-8859-3	9	1			1	11
ISO-8859-10	10	0			0	10
KSC_5601	3	6			0	9
GBK	4	2			0	6
windows-1253	0	3	0	0	2	5
ISO-8859-5	1	0	0		3	4
IBM850	0	4			0	4
windows-1257	0	3			0	3
ISO-2022-JP-2	2	0			0	2
ISO-8859-6	0	1	0		0	1
Total	421,751	536,373	2,226	11,523	44,892	1,016,765

The most obvious thing shown in this table is that the most common charsets remain ISO-8859-1, Windows-1252, US-ASCII, UTF-8, and ISO-8859-15, which is to be expected, given an expected prior bias to European languages in newsgroups. The low prevalence of ISO-2022-JP is surprising to me: it means a lower incidence of Japanese than I would have expected. Either that, or Japanese have switched to UTF-8 en masse, which I consider very unlikely given that Japanese have tended to resist the trend towards UTF-8 the most.

Beyond that, this dataset has caused me to lose trust in the ICU charset detectors. KOI8-R is recorded as being 18% malformed text, with most of that ICU believing to be ISO-8859-1 instead. Judging from the results, it appears that ICU has a bias towards guessing ISO-8859-1, which means I don't believe the numbers in the Other column to be accurate at all. For some reason, I don't appear to have decoders for ISO-8859-16 or x-mac-croatian on my local machine, but running some tests by hand appear to indicate that they are valid and not incorrect.

Somewhere between 0.1% and 1.0% of all messages are subject to mojibake, depending on how much you trust the charset detector. The cases of UTF-8 being misdetected as non-UTF-8 could potentially be explained by having very few non-ASCII sequences (ICU requires four valid sequences before it confidently declares text UTF-8); someone who writes a post in English but has a non-ASCII signature (such as myself) could easily fall into this category. Despite this, however, it does suggest that there is enough mojibake around that users need to be able to override charset decisions.

The undeclared charsets are described, in descending order of popularity, by ISO-8859-1, Windows-1252, KOI8-R, ISO-8859-2, and UTF-8, describing 99% of all non-ASCII undeclared data. ISO-8859-1 and Windows-1252 are probably over-counted here, but the interesting tidbit is that KOI8-R is used half as much undeclared as it is declared, and I suspect it may be undercounted. The practice of using locale-default fallbacks that Thunderbird has been using appears to be the best way forward for now, although UTF-8 is growing enough in popularity that using a specialized detector that decodes as UTF-8 if possible may be worth investigating (3% of all non-ASCII, undeclared messages are UTF-8).

HTML

Unsuprisingly (considering I'm polling newsgroups), very few messages contained any HTML parts at all: there were only 1,032 parts in the total sample size, of which only 552 had non-ASCII characters and were therefore useful for the rest of this analysis. This means that I'm skeptical of generalizing the results of this to email in general, but I'll still summarize the findings.

HTML, unlike plain text, contains a mechanism to explicitly identify the charset of a message. The official algorithm for determining the charset of an HTML file can be described simply as "look for a tag in the first 1024 bytes. If it can be found, attempt to extract a charset using one of several different techniques depending on what's present or not." Since doing this fully properly is complicated in library-less C++ code, I opted to look first for a tt> production, guess the extent of the tag, and try to find a charset= string somewhere in that tag. This appears to be an approach which is more reflective of how this parsing is actually done in email clients than the proper HTML algorithm. One difference is that my regular expressions also support the newer construct, although I don't appear to see any use of this.

I found only 332 parts where the HTML declared a charset. Only 22 parts had a case where both a MIME charset and an HTML charset and the two disagreed with each other. I neglected to count how many messages had HTML charsets but no MIME charsets, but random sampling appeared to indicate that this is very rare on the data set (the same order of magnitude or less as those where they disagreed).

As for the question of who wins: of the 552 non-ASCII HTML parts, only 71 messages did not have the MIME type be the valid charset. Then again, 71 messages did not have the HTML type be valid either, which strongly suggests that ICU was detecting the incorrect charset. Judging from manual inspection of such messages, it appears that the MIME charset ought to be preferred if it exists. There are also a large number of HTML charset specifications saying unicode, which ICU treats as UTF-16, which is most certainly wrong.

Headers

In the data set, 1,025,856 header blocks were processed for the following statistics. This is slightly more than the number of messages since the headers of contained message/rfc822 parts were also processed. The good news is that 97% (996,103) headers were completely ASCII. Of the remaining 29,753 headers, 3.6% (1,058) were UTF-8 and 43.6% (12,965) matched the declared charset of the first body part. This leaves 52.9% (15,730) that did not match that charset, however.

Now, NNTP messages can generally be expected to have a higher 8-bit header ratio, so this is probably exaggerating the setup in most email messages. That said, the high incidence is definitely an indicator that even non-EAI-aware clients and servers cannot blindly presume that headers are 7-bit, nor can EAI-aware clients and servers presume that 8-bit headers are UTF-8. The high incidence of mismatching the declared charset suggests that fallback-charset decoding of headers is a necessary step.

RFC 2047 encoded-words is also an interesting statistic to mine. I found 135,951 encoded-words in the data set, which is rather low, considering that messages can be reasonably expected to carry more than one encoded-word. This is likely an artifact of NNTP's tendency towards 8-bit instead of 7-bit communication and understates their presence in regular email.

Counting encoded-words can be difficult, since there is a mechanism to let them continue in multiple pieces. For the purposes of this count, a sequence of such words count as a single word, and I indicate the number of them that had more than one element in a sequence in the Continued column. The 2047 Violation column counts the number of sequences where decoding words individually does not yield the same result as decoding them as a whole, in violation of RFC 2047. The Only ASCII column counts those words containing nothing but ASCII symbols and where the encoding was thus (mostly) pointless. The Invalid column counts the number of sequences that had a decoder error.

Charset	Count	Continued	2047 Violation	Only ASCII	Invalid
ISO-8859-1	56,355	15,610		499	0
UTF-8	36,563	14,216	3,311	2,704	9,765
ISO-8859-15	20,699	5,695		40	0
ISO-8859-2	11,247	2,669		9	0
windows-1252	5,174	3,075		26	0
KOI8-R	3,523	1,203		12	0
windows-1256	765	568		0	0
Big5	511	46	28	0	171
ISO-8859-7	165	26		0	3
windows-1251	157	30		2	0
GB2312	126	35	6	0	51
ISO-2022-JP	102	8	5	0	49
ISO-8859-13	78	45		0	0
ISO-8859-9	76	21		0	0
ISO-8859-4	71	2		0	0
windows-1250	68	21		0	0
ISO-8859-5	66	20		0	0
US-ASCII	38	10		38	0
TIS-620	36	34		0	0
KOI8-U	25	11		0	0
ISO-8859-16	22	1		0	22
UTF-7	17	2	1	8	3
EUC-KR	17	4	4	0	9
x-mac-croatian	10	3		0	10
Shift_JIS	8	0	0	0	3
Unknown	7	2		0	7
ISO-2022-KR	7	0	0	0	0
GB18030	6	1	0	0	1
windows-1255	4	0		0	0
ISO-8859-14	3	0		0	0
ISO-8859-3	2	1		0	0
GBK	2	0	0	0	2
ISO-8859-6	1	1		0	0
Total	135,951	43,360	3,361	3,338	10,096

This table somewhat mirrors the distribution of regular charsets, with one major class of differences: charsets that represent non-Latin scripts (particularly Asian scripts) appear to be overdistributed compared to their corresponding use in body parts. The exception to this rule is GB2312 which is far lower than relative rankings would presume—I attribute this to people using GB2312 being more likely to use 8-bit headers instead of RFC 2047 encoding, although I don't have direct evidence.

Clearly continuations are common, which is to be relatively expected. The sad part is how few people bother to try to adhere to the specification here: out of 14,312 continuations in languages that could violate the specification, 23.5% of them violated the specification. The mode-shifting versions (ISO-2022-JP and EUC-KR) are basically all violated, which suggests that no one bothered to check if their encoder "returns to ASCII" at the end of the word (I know Thunderbird's does, but the other ones I checked don't appear to).

The number of invalid UTF-8 decoded words, 26.7%, seems impossibly high to me. A brief check of my code indicates that this is working incorrectly in the face of invalid continuations, which certainly exaggerates the effect but still leaves a value too high for my tastes. Of more note are the elevated counts for the East Asian charsets: Big5, GB2312, and ISO-2022-JP. I am not an expert in charsets, but I belive that Big5 and GB2312 in particular are a family of almost-but-not-quite-identical charsets and it may be that ICU is choosing the wrong candidate of each family for these instances.

There is a surprisingly large number of encoded words that encode only ASCII. When searching specifically for the ones that use the US-ASCII charset, I found that these can be divided into three categories. One set comes from a few people who apparently have an unsanitized whitespace (space and LF were the two I recall seeing) in the display name, producing encoded words like =?us-ascii?Q?=09Edward_Rosten?=. Blame 40tude Dialog here. Another set encodes some basic characters (most commonly = and ?, although a few other interpreted characters popped up). The final set of errors were double-encoded words, such as =?us-ascii?Q?=3D=3FUTF-8=3FQ=3Ff=3DC3=3DBCr=3F=3D?=, which appear to be all generated by an Emacs-based newsreader.

One interesting thing when sifting the results is finding the crap that people produce in their tools. By far the worst single instance of an RFC 2047 encoded-word that I found is this one: Subject: Re: [Kitchen Nightmares] Meow! Gordon Ramsay Is =?ISO-8859-1?B?UEgR lqZ VuIEhlYWQgVH rbGeOIFNob BJc RP2JzZXNzZW?= With My =?ISO-8859-1?B?SHVzYmFuZ JzX0JhbGxzL JfU2F5c19BbXiScw==?= Baking Company Owner (complete with embedded spaces), discovered by crashing my ad-hoc base64 decoder (due to the spaces). The interesting thing is that even after investigating the output encoding, it doesn't look like the text is actually correct ISO-8859-1... or any obvious charset for that matter.

I looked at the unknown charsets by hand. Most of them were actually empty charsets (looked like =??B?Sy4gSC4gdm9uIFLDvGRlbg==?=), and all but one of the outright empty ones were generated by KNode and really UTF-8. The other one was a Windows-1252 generated by a minor newsreader.

Another important aspect of headers is how to handle 8-bit headers. RFC 5322 blindly hopes that headers are pure ASCII, while RFC 6532 dictates that they are UTF-8. Indeed, 97% of headers are ASCII, leaving just 29,753 headers that are not. Of these, only 1,058 (3.6%) are UTF-8 per RFC 6532. Deducing which charset they are is difficult because the large amount of English text for header names and the important control values will greatly skew any charset detector, and there is too little text to give a charset detector confidence. The only metric I could easily apply was testing Thunderbird's heuristic as "the header blocks are the same charset as the message contents"—which only worked 45.2% of the time.

Encodings

While developing an earlier version of my scanning program, I was intrigued to know how often various content transfer encodings were used. I found 1,028,971 parts in all (1,027,474 of which are text parts). The transfer encoding of binary did manage to sneak in, with 57 such parts. Using 8-bit text was very popular, at 381,223 samples, second only to 7-bit at 496,114 samples. Quoted-printable had 144,932 samples and base64 only 6,640 samples. Extremely interesting are the presence of 4 illegal transfer encodings in 5 messages, two of them obvious typos and the others appearing to be a client mangling header continuations into the transfer-encoding.

Conclusions

So, drawing from the body of this data, I would like to make the following conclusions as to using charsets in mail messages:

1. Have a fallback charset. Undeclared charsets are extremely common, and I'm skeptical that charset detectors are going to get this stuff right, particularly since email can more naturally combine multiple languages than other bodies of text (think signatures). Thunderbird currently uses a locale-dependent fallback charset, which roughly mirrors what Firefox and I think most web browsers do.
2. Let users override charsets when reading. On a similar token, mojibake text, while not particularly common, is common enough to make declared charsets sometimes unreliable. It's also possible that the fallback charset is wrong, so users may need to override the chosen charset.
3. Testing is mandatory. In this set of messages, I found base64 encoded words with spaces in them, encoded words without charsets (even UNKNOWN-8BIT), and clearly invalid Content-Transfer-Encodings. Real email messages that are flagrantly in violation of basic spec requirements exist, so you should make sure that your email parser and client can handle the weirdest edge cases.
4. Non-UTF-8, non-ASCII headers exist. EAI not withstanding, 8-bit headers are a reality. Combined with a predilection for saying ASCII when text is really ASCII, this means that there is often no good in-band information to tell you what charset is correct for headers, so you have to go back to a fallback charset.
5. US-ASCII really means ASCII. Email clients appear to do a very good job of only emitting US-ASCII as a charset label if it's US-ASCII. The sample size is too small for me to grasp what charset 8-bit characters should imply in US-ASCII.
6. Know your decoders. ISO-8859-1 actually means Windows-1252 in practice. Big5 and GB1232 are actually small families of charsets with slightly different meanings. ICU notably disagrees with some of these realities, so be sure to include in your tests various charset edge cases so you know that the decoders are correct.
7. UTF-7 is still relevant. Of the charsets I found not mentioned in the WHATWG encoding spec, IBM437 and x-mac-croatian are in use only due to specific circumstances that limit their generalizable presence. IBM850 is too rare. UTF-7 is common enough that you need to actually worry about it, as abominable and evil a charset it is.
8. HTML charsets may matter—but MIME matters more. I don't have enough data to say if charsets declared in HTML are needed to do proper decoding. I do have enough to say fairly conclusively that the MIME charset declaration is authoritative if HTML disagrees.
9. Charsets are not languages. The entire reason x-mac-croatian is used at all can be traced to Thunderbird displaying the charset as "Croatian," despite it being pretty clearly not a preferred charset. Similarly most charsets are often enough ASCII that, say, an instance of GB2312 is a poor indicator of whether or not the message is in English. Anyone trying to filter based on charsets is doing a really, really stupid thing.
10. RFCs reflect an ideal world, not reality. This is most notable in RFC 2047: the specification may state that encoded words are supposed to be independently decodable, but the evidence is pretty clear that more clients break this rule than uphold it.
11. Limit the charsets you support. Just because your library lets you emit a hundred charsets doesn't mean that you should let someone try to do it. You should emit US-ASCII or UTF-8 unless you have a really compelling reason not to, and those compelling reasons don't require obscure charsets. Some particularly annoying charsets should never be written: EBCDIC is already basically dead on the web, and I'd like to see UTF-7 die as well.

When I have time, I'm planning on taking some of the more egregious or interesting messages in my dataset and packaging them into a database of emails to help create testsuites on handling messages properly.

http://quetzalcoatal.blogspot.com/2014/03/understanding-email-charsets.html