Using social media to measure intentions to move house

Is it possible to measure the intentions of people to move house without actually asking them about their plans? Statistics Netherlands (CBS) has researched this question at the Center for Big Data Statistics. On the instructions of the Ministry of the Interior and Kingdom Relations, CBS already asks about the intention to move in the WoON survey, which forms part of the ongoing housing research. Every three years, this survey uses a questionnaire to gather information on respondents’ current living situation, including satisfaction with their homes and living environments, and their housing needs.

The ministry has commissioned CBS to investigate the use of alternative sources, such as social media, to measure the moving intentions. This CBS study shows that it is possible to automatically distil the moving intentions from information in social media posts. This is not to say that these people actually will move house in the near future, or that these people’s intention of moving reflects the intention of moving among the population of the Netherlands as a whole. Because the results deviate from the trends established in surveys and registers, they are not considered suitable to replace this data.

Working method

To test whether it is possible to measure the intention of moving using social media, the Center for Big Data Statistics has studied a large number of public social media posts written in Dutch. These items were posted between 1 January 2011 and the end of May 2018. As far as possible, posts written by people from Flanders and from countries other than the Netherlands were filtered out of the results.

The starting point for the study was a randomly selected set of posts containing the partial words ‘verhuis’ or ‘verhuiz’ (the first part of the Dutch words for ‘move house’). All the selected posts include at least one instance of a word containing this combination of letters. Two independent coders then manually classified this set of posts into two categories: ‘includes intention of moving’ and ‘includes no intention of moving’. This classification was uncontested in 95 per cent of cases. Cluster analysis was used to identify even more posts that included an intention of moving. These were classified manually. In total, 12,353 posts containing ‘verhuis’ and/or ‘verhuiz’ were studied, of which 4,324 (35 per cent) were identified as showing a moving intention. The coding instructions followed were that:
1. The post must originate from a private individual and not from a company;
2. The intention of moving must relate to the person who posted the message or to this person’s family;
3. The intention of moving must refer to a physical relocation to another country, municipality or house, rather than for instance the relocation of a website to another URL;
4. The move has not yet taken place;
5. The post must represent a serious intention of moving and not be meant sarcastically or as a joke.

This classified set of posts was then used to develop a model capable of identifying, with the greatest possible accuracy, social media posts that contain an intention of moving. This model uses logistical regression, which enables the model to choose the words, combinations of words or post characteristics that offer the best likelihood of detecting a moving intention. The classified data set was used as a training and test set. The model was compared to several alternative methods using machine learning. When it became clear that these methods were as reliable as or less reliable than the model, the decision was made to use the simplest model based on regression analysis. This model correctly identified whether or not a post contained an intention of moving in 87 per cent of cases. The first tests of the models were conducted using all social media platforms. To comply with applicable privacy legislation, however, it was decided that these analyses would be based exclusively on Twitter, excluding other platforms such as Facebook.

Findings

All public tweets containing the words ‘verhuis’ and/or ‘verhuiz’ that were available between 1 January 2011 and the end of May 2018 were included in the analyses: over 1.9 million tweets. In total, more than 4.8 billion Dutch-language tweets were available during this period. These tweets were used to assess the development of a moving intention in that time. The model used to classify tweets as ‘includes intention of moving’ or ‘includes no intention of moving’ is considered sufficiently reliable to be able to identify the intention of moving.

Figure 1 displays the findings from the relative number of tweets containing an intention of moving in the period from January 2011 to May 2018 inclusive. The series clearly shows two periods in which there is a decrease in the number of tweets containing an intention of moving: from January 2011 to December 2013 inclusive, and from August 2014 to May 2018 inclusive. The intervening period shows an increase in tweets regarding the intention of moving. As well as conducting an analysis in time, it is also interesting to see whether there is a seasonal pattern. The analysis showed that there is no strongly visible seasonal pattern, although the number of tweets containing a moving intention is lower in the period around December.

On average, 133 tweets including a moving intention were found each day during the period analysed, which translates to around 930 per week. In total more than 1.7 million tweets were posted every day: around 12 million a week. This puts the percentage of tweets including an intention of moving at 0.008 per cent after rounding. The series of tweets including an intention of moving reveals a different trend from that of the total number of tweets. This means that in some way the data reflects something meaningful. The daily and weekly series show the same patterns over time. The question, then, is whether an unusual occurrence such as an intention to relocate can be picked up with sufficient accuracy from social media. For validation purposes, 1,000 randomly-selected, anonymised social media accounts were followed for two years. Half the people who tweeted during this period indicated at a certain point that they had actually moved house.

Figure 1. Relative number of tweets containing an intention of moving posted each day from 1 January 2011 to 31 May 2018 inclusive (the inset shows the data before applying the spline filter)

Tweets containing a moving intention (x 1,000)
 Moving intentions
1-1-'11191.3
2-1-'1118945.2
3-1-'1118768.3
4-1-'1118596.6
5-1-'111843
6-1-'1118267.8
7-1-'1118110.4
8-1-'1117957.5
9-1-'1117808.8
10-1-'1117664.1
11-1-'1117523.4
12-1-'1117386.4
13-1-'1117252.8
14-1-'1117122.7
15-1-'1116995.6
16-1-'1116871.7
17-1-'1116751
18-1-'1116633.9
19-1-'1116520.6
20-1-'1116411.5
21-1-'1116306.7
22-1-'1116206.7
23-1-'1116111.7
24-1-'1116021.9
25-1-'1115937.8
26-1-'1115859.5
27-1-'1115787.3
28-1-'1115721.6
29-1-'1115662.6
30-1-'1115610.6
31-1-'111556.6
1-2-'1115528.6
2-2-'1115498.8
3-2-'1115476.6
4-2-'1115462.2
5-2-'1115455.5
6-2-'1115456.8
7-2-'1115466.2
8-2-'1115483.7
9-2-'1115509.4
10-2-'111554.3
11-2-'1115586.1
12-2-'1115637.2
13-2-'1115697
14-2-'1115765.6
15-2-'1115842.7
16-2-'1115927.2
17-2-'1116017.4
18-2-'1116111.7
19-2-'1116208.5
20-2-'1116306.3
21-2-'1116403.3
22-2-'1116498.2
23-2-'1116589.2
24-2-'1116674.7
25-2-'1116753.2
26-2-'1116823.1
27-2-'1116882.8
28-2-'1116930.7
1-3-'1116965.5
2-3-'111698.7
3-3-'1116995.8
4-3-'111699.2
5-3-'1116975
6-3-'1116945.9
7-3-'111690.5
8-3-'111685.2
9-3-'1116786.7
10-3-'1116710.3
11-3-'1116622.6
12-3-'1116523.8
13-3-'1116414
14-3-'1116293.6
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16-3-'1116021.8
17-3-'1115873.1
18-3-'1115718.9
19-3-'1115561.7
20-3-'111540.4
21-3-'1115247.7
22-3-'1115095.9
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24-3-'1114814.4
25-3-'1114689.7
26-3-'1114578.8
27-3-'1114484.2
28-3-'1114408.3
29-3-'1114353.5
30-3-'1114322.2
31-3-'1114315.8
1-4-'1114332
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3-4-'1114417.7
4-4-'1114480.3
5-4-'1114551.4
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11-4-'1114962.5
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19-4-'1114752.9
20-4-'1114686.5
21-4-'1114623.4
22-4-'1114567.1
23-4-'1114521.5
24-4-'1114490.1
25-4-'1114476.9
26-4-'1114485.3
27-4-'1114519.3
28-4-'1114582.4
29-4-'1114677.4
30-4-'1114803
1-5-'1114957.1
2-5-'1115137.4
3-5-'1115341.6
4-5-'1115567.6
5-5-'1115813
6-5-'1116075.6
7-5-'1116353.3
8-5-'1116643.8
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11-5-'111756.9
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1-6-'1121034.4
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30-6-'111370.2
1-7-'1113712.8
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11-7-'1114387
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1-8-'1113357.2
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25-8-'1111232.7
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28-8-'1111111.2
29-8-'1111076.9
30-8-'1111045.6
31-8-'1111017.3
1-9-'1110991.9
2-9-'1110969.5
3-9-'1110950
4-9-'111093.3
5-9-'1110919.4
6-9-'1110908.2
7-9-'1110899.8
8-9-'1110894.1
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10-9-'1110892
11-9-'1110896.1
12-9-'1110904.1
13-9-'1110916.2
14-9-'1110932.7
15-9-'1110954
16-9-'1110980.2
17-9-'1111011.8
18-9-'1111048.9
19-9-'1111091.8
20-9-'1111141
21-9-'1111196.6
22-9-'1111258.9
23-9-'1111328
24-9-'1111403.5
25-9-'1111484.4
26-9-'1111570.1
27-9-'1111659.8
28-9-'1111752.7
29-9-'111184.8
30-9-'1111945.3
1-10-'1112043.4
2-10-'1112141.7
3-10-'1112239.5
4-10-'1112335.9
5-10-'1112430.3
6-10-'1112521.9
7-10-'1112609.9
8-10-'1112693.7
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10-10-'1112848.4
11-10-'1112919.2
12-10-'1112985.8
13-10-'1113048.2
14-10-'1113106.3
15-10-'111316
16-10-'1113209.7
17-10-'1113255.1
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21-10-'1113394.9
22-10-'1113419.4
23-10-'1113440
24-10-'1113457.2
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27-10-'1113496.3
28-10-'1113507.8
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30-10-'1113533.1
31-10-'1113548.5
1-11-'111356.7
2-11-'1113588.5
3-11-'1113614.7
4-11-'1113646
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6-11-'1113726.8
7-11-'1113774.5
8-11-'1113824.1
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11-11-'1113959.8
12-11-'1113992.5
13-11-'1114015
14-11-'1114024.8
15-11-'1114019.4
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18-11-'1113888.3
19-11-'1113798.1
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21-11-'1113534.6
22-11-'1113361.7
23-11-'1113165.3
24-11-'1112948.4
25-11-'1112714
26-11-'1112465.1
27-11-'1112204.9
28-11-'1111936.4
29-11-'1111662.5
30-11-'1111386.5
1-12-'111111.1
2-12-'1110840
3-12-'1110575.6
4-12-'1110321.2
5-12-'1110079.8
6-12-'119854
7-12-'119644.5
8-12-'119451.4
9-12-'119274.8
10-12-'119115
11-12-'118971.9
12-12-'118845.9
13-12-'118736.9
14-12-'118645.3
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16-12-'118514.6
17-12-'118475.7
18-12-'118454.8
19-12-'118451.9
20-12-'11846.7
21-12-'118497.9
22-12-'118543.1
23-12-'118600.5
24-12-'118668
25-12-'118743.6
26-12-'118825.3
27-12-'118911
28-12-'118998.8
29-12-'119086.7
30-12-'119172.5
31-12-'11925.4
1-1-'129330.1
2-1-'12939.8
3-1-'129455.6
4-1-'129501.7
5-1-'129536.7
6-1-'129561.3
7-1-'129576.7
8-1-'129583.5
9-1-'129582.8
10-1-'129575.4
11-1-'129562.3
12-1-'129544.4
13-1-'129522.5
14-1-'129497.6
15-1-'129470.6
16-1-'129442.3
17-1-'129413.7
18-1-'129385.8
19-1-'129359.2
20-1-'129334.6
21-1-'129312.3
22-1-'129292.9
23-1-'129276.8
24-1-'129264.3
25-1-'129256.1
26-1-'129252.5
27-1-'129253.9
28-1-'12926.1
29-1-'129273.8
30-1-'129293.1
31-1-'129319.3
1-2-'129352.7
2-2-'129393.7
3-2-'129441.3
4-2-'129494.5
5-2-'12955.2
6-2-'129613.4
7-2-'129677
8-2-'129741.8
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1-3-'1210073.7
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30-3-'129231
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1-4-'129192.6
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1-5-'129824.2
2-5-'129865
3-5-'129904.2
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6-5-'1210005.5
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