How To Create Backlinks Naturally?

As We all know importance of backlinks in gaining high rank on google so people generally starts creating backlinks rapidly which does nothing but only hurt their search engine rankings for creating backlinks one need to have patience and try to gain backlinks naturally because that’s what google and other search engine wants:-

So hear are some following methods of creating backlinks naturally i hope its helpful to everyone:

Article directories: Article directories are one of the best and trusted way of gaining backlinks naturally as you submit your content with your link pointing toward your website on these directories your content is picked up by many webmasters and bloggers along with the link that you have added on your article thus creating links naturally.

Tips On How To Create Unique Content?

Unique content’s a good thing, right? Not always.

To get well written text, you need to invest some time, or pay someone skilled to do it for you. Both are expensive and slow exercises, so many SEOs choose to take a shortcut and “spin” articles. This generally involves taking one source text, and altering the language inside it to “create” a new article. The better spinning programs are aware of things like grammar rules, word frequencies in various languages, set phrases and idioms, and so forth. The worse – and predominant – spinners simply perform synonym replacement, which produces this kind of mapping:

“We walked to the large house” would be replaced by “We ambulated to the gigantic residence”.

The latter looks ugly, is awkward to read, and generally doesn’t fulfil any kind of quality standard – though it is different, thus helping create unique content, and the meaning remains pretty much the same. The massive failing here, though, that utterly defeats the goal of those using spinners, is how trivial the product is to detect.

In every language, there are common words, rare words, and everything between. The probability of individual words occuring in a piece of text is fairly constant, being skewed a bit depending on the document’s type and domain (agricultural reports will be more likely to contain terms about farming, horticulture, and plant and chemical proper nouns, for example). The core set of terms, and their frequencies, will remain the same.

The direct synonym replacement used by typical spinning programs (spinners) has two problems. Firstly, one must bear context in mind when picking alternative words. For example, “junk” (used as a noun) can also be:

“boat, clutter, debris, discard, dope, dreck, dump, flotsam, garbage, jetsam, jettison, litter, refuse, rubbish, salvage, scrap, ship, trash, waste”

Depending on whether we’re using this word to talk about a sailing vessel or a piece of rubbish, we can divide this set of alternatives into two distinct groups with different meanings. Simply using a thesaurus to pick a random replacement word will often change the meaning of a sentence. “I thought your product was a heap of junk” is not semantically equivalent to “I thought your product was a heap of boat”. Note how simplistic substitution also makes the sentence grammatically incorrect in this case.

The second problem with direct synonym replacement is that it doesn’t care about the probability distribution of words in a language. This always leads to the inclusion of rarer words, and exclusion of more common ones. In one above example, we used ambulate instead of walk; the former is a comparatively rare word. Using it makes the sentence more awkward, and harder to read (protip: always use the simplest language that you can).

Just to show how easy spun pages are to spot, let’s find one, and take it apart, then see how abnormal it is. We’re going to first find how frequent words are in English, and then use them to compare a spun article to a previous post on my blog.

The reference frequency list we use to represent general English comes from the British National Corpus. This is in British English, so we’ll make things fairer by Anglicising the spun document, making “color” into “colour”, “center” into “centre”, and “-ize” into “-ise”.

We’ll mathematically compare both the spun and un-spun text against this reference model of the English language. This can be done by first building a list of words used in a document, and then counting how many times this occurs in the document. Dividing the count of a word by the total number gives the probability than any random token from the document will match that word. We’ll also have a list of these probabilities from the British National Corpus (BNC). To compare this, we’ll take the absolute difference between measured and reference probabilities for each term, and express that as a percentage of the reference likelihood. As these percentages get pretty high, and to reduce the impact of any anomalous data, we’ll also measure the log of the difference measure.

Spun document

Taken from Good Articles Recommend Top Rank by SEO, an almost illegible and probably spun document (it may possibly be badly translated by someone with a newfound love for thesaurii, though given the topic domain – SEO – this seems only minimally likely).word freq prob bncprob difference logdiff
seo 5 0.0125 9.00E-09 138888791.10% 6.142667198
spell-check 1 0.0025 1.80E-08 13888789.11% 5.142664384
overusing 1 0.0025 1.90E-08 13157794.88% 5.119183112
copywriting 2 0.005 5.70E-08 8771829.78% 4.943090196
scruffily 1 0.0025 5.90E-08 4237188.04% 4.627077738
overeat 1 0.0025 8.80E-08 2840809.03% 4.453442039
well-crafted 1 0.0025 1.38E-07 1811494.22% 4.258036952
proofread 1 0.0025 3.05E-07 819572.12% 3.913587174

Full dataset

We can see a few words sticking out here. Some give an indication of the document’s topic (SEO, copywriting) while others are quite bizarre (scruffily, overeat). The difference column shows the magnitude of frequency variation from what’s expected – a difference of zero means that a word occurs just as frequently in this text as it does in the British National Corpus; a difference of 100% means that the word occurs twice as often or half as often. Note how the words that stick out hugely aren’t that congruent as a set – overeating and scruffiness have little to do with copywriting, spell checking and proof reading.

Differences measured this way will be skewed rapidly by any rarer words that come into a document, and every document that has something to say will have to incorporate some topics using rarer that don’t fit the curve perfectly – this would be expected. So, using this measure, a non-zero difference score is inevitable; logs have been taken to smooth differences in scale. What is significant is where and how much the differences are.

The mean difference from standard English for the unspun document is 947349.85%, and the mean of the logs of the difference measure is ~1.46. These numbers show us how different the words in the spun document are from what would be expected in general language.

Unspun document

Taken from my overall vaguely positive Ubercart review.word freq prob bncprob difference logdiff
cron 2 0.002444988 9.00E-09 27166431.27% 5.434032591
www 2 0.002444988 1.90E-08 12868256.85% 5.109519721
php 1 0.001222494 2.90E-08 4215396.07% 4.624838387
firewall 1 0.001222494 3.80E-08 3216989.14% 4.507449594
uploading 1 0.001222494 3.90E-08 3134499.74% 4.496168238
todos 1 0.001222494 4.80E-08 2546762.24% 4.405988403
upload 1 0.001222494 5.60E-08 2182924.83% 4.33903878
metadata 1 0.001222494 5.80E-08 2107648.07% 4.323798095
poin 1 0.001222494 5.80E-08 2107648.07% 4.323798095

Full dataset

We can guess from the top differences here that the document is related to computing and fairly technical. The biggest differences in word probability are in the range of say 1e6 – 2.7e7, a lot less than the top four in the unspun document, which were from 8e6 all the way up to 1.4e8. The mean difference is half that of the unspun document (524475.55%) and the log differences again significantly smaller (1.18).

Comparison

For good measure, and to illustrate this point clearly, here’s a graph. The red line is the spun document, the blue one the unspun one. For a document that completely followed average word frequency, you’d see a line at y=0 (i.e., a flatline).

This shows that the spun document uses English consistently more unusually than the human-written (unspun) document; the red line is higher than the blue one, and the higher a point is, the more it varies from the British National Corpus’ survey of English usage. For reference, that covers ~10 million words in 4000 documents, so it’s a fairly good source of comparison data.

We all know about term frequencies (TF); it seems fair to guess that search engines have models of these, and that’s it’s not computationally intense for them to use TF as one tool to distinguish spam from useful content. When one can pick out spun content so easily (this system took ~40 minutes of coding and juggling in excel to make it look pretty, for one guy), there’s really no point bothering to add it to your site.

Of course, a sophisticated document spinner is definitely possible to construct. My point here is, the cheap and common ones only provide a massive bright flag that your site is spam. Avoid them.

Data

A full set of all the produced data, in a pretty and readable format, including the full keyword data, and a large graph, is available online here. The texts actually used for comparison are here (unspun) and here (spun).

Further information

If you feel like exploring English word frequences and getting into that long tail, I can’t recommend anything more highly than Wordcount.org.

Tips On getting free backlinks from blogger

Blogger, like many other popular services, offers an option to post-by-email. This lets you post when you’re out and about. You pick a special email address, and when you send something there, it appears on your blog; the subject is the blog title, and the content is the blog post. Blogger’s service doesn’t offer HTML, but it will auto-link any URLs that you put in the email subject.

According to Blogger’s help:
The format of the email address is username.secretword@blogger.com . Note that this email address must be kept secret.

Of course, a few of these have leaked into the public domain, onto mailing lists, which is why you see some sites at blogspot filled with ads for cialis and the like. One of these, for battlefornaboo.blogspot.com, is “robitforrock dot starwars at blogger dot com”. Enjoy!

How To See Over More Than 1000 On Google

Sometimes we want to get a huge list of URLs from a search engine. For example, you might want to find all the pages linking back to you. However, Google won’t return anything past 1000 results; see this search for “south”; we’re on page 99, with 10 results per page – and if you scroll to the bottom, you’ll see that page numbers run out. So – how can we get more results?

One technique for measuring the propreties of words and phrases in text is n-gram analysis. This counts the numbers of single-word (unigram), two-word (bigram), three-word etc (up to n-word) phrases in a text.

E.g.: given the phrase “The cat sat on the mat”, we have the following unigrams:
The – 2
Cat – 1
Sat – 1
On – 1
Mat – 1
And the following bigrams:
The cat – 1
cat sat – 1
sat on – 1
on the – 1
the mat – 1
So how does this help us? Well, n-gram counts of large amounts of text tell us what the most common words we’ll find are. Once we ignore stopwords (the search engines will), we get terms that we can use as part of a search query to split up the results. If we know that “fish” and “knee” are common words, we could run two queries:
link:mysite.com knee
link:mysite.com fish
This would return 2000 links to mysite.com. Of course, some of these pages will have the words both “fish” and “knee” on, so there’ll be some kind of overlap, but we’ll still get say 1700-1900 useful unique sites. Once we have a good list to exploit, we can take the top 1000 results for our query divided up with 40 different ngrams to get a good 25000-35000 results – way past the 1000 limit usually imposed.

Implementing something like this would probably look like:

URL table – with unique URL field

query = “link:competitor.com”
for ngram in ngrams
for page = 1, page < 10, page ++
offset = (page – 1) * 10
results = getGoogleResults(query + ” ” + ngram, offset)
for result in results
sql(“insert ignore into URL values(?)”, result)

Of course, this is massively open to optimisation; post in the comments if you have any questions.

To help you out, I’ve included a list of over 700 of the most common English unigrams, derived from a good web-based source. If you’re interested in versions in other languages, or a longer list, let me know why and I’ll see what I can do.

Tips On Viewing HTTP Header

HTTP headers are the part of the webpage you don’t see in your browser (usually); the special data describing the page to your browser software. HTTP headers are wehere you’d put redirects, information about whether a file is a PNG / HTML page / RAR archive, and where you say if the browser should open the file or present it as a download – as well as many other things. The full details are in RFC2616.

I’m going to cover three methods of dealing with headers today – all quick, simple and powerful.

Perl’s LWP “HEAD” command

This is a commonly-found *nix command line tool, very simple in its operation, and likely already on your system. To use it, you simply enter “HEAD ” at the command prompt, where is the full address (e.g. including http:) that you want to check.

If you don’t have it, you can install this as root by whipping up a CPAN console (perl -MCPAN -e shell) and running i LWP::Simple – then just follow the prompts, and opt to install the GET/HEAD aliases.

Quick and simple – but it won’t report on redirects, just the final page, and you need root to install it in most circumstances.

Tamper Data

You can use Firefox to examine headers, alter HTTP requests, and find out precisely what every page is doing with this masterpiece of a plugin. If you’re using LiveHttpHeaders, I suggest you immediately exchange it for Tamper Data – just as light, and much more powerful. To use this, simply enable Tamper Data in Firefox, click “Start tampering” in the new window, and then visit the page you’re interested in; you don’t want to go playing with the server immediately, so simply accept the request, and ignore further requests. Tada – more information than you’ll ever need – including full request and response headers for everything on the page! This is also great for finding out FLV URLs and other things hidden by Flash apps.

Tamper Data has many additional functions, including page load optimisation, and far too much to cover here. Just check out this tutorial for a taster.

Command-line cURL header script

For a very verbose, quick, minimal and to the point solution, create a file called header somewhere on your *nix server, and fill it thusly (perhaps amending the PHP executable path):

#!/usr/bin/env php
$url = $argv[1];

function url_header($url) {
global $useragent;
global $timeout;
if ($useragent == “”) {$useragent = “Mozilla 8.0 +http://seorant.blogspot.com”;}
if ($timeout == “”) {$timeout = 20;}
$ch = curl_init();
curl_setopt ($ch, CURLOPT_URL, $url);
curl_setopt ($ch, CURLOPT_USERAGENT, $useragent);
curl_setopt ($ch, CURLOPT_HEADER, 1);
curl_setopt ($ch, CURLOPT_NOBODY, 1);
curl_setopt ($ch, CURLOPT_RETURNTRANSFER, 1);
curl_setopt ($ch, CURLOPT_FOLLOWLOCATION, 1);
curl_setopt ($ch, CURLOPT_TIMEOUT, $timeout);
curl_setopt ($ch, CURLOPT_MUTE, 1);
$result = curl_exec ($ch);
curl_close($ch);
return $result;
}

$useragent = ‘Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.7) Gecko/20040614 Firefox/0.8′;

echo $url.”\n\n”; flush();
echo url_header($url);
?>

Make the file executable (chmod u+x header) and run with ./header

This will include the full details of redirects as and when they’re performed, and should get a reponse more akin to that a browser receives when compared to the LWP method, which uses a different useragent string.