We are grateful to the clients and staff of Jobs, Friends and Houses, UK, who supported and agreed to participate in our research.

The research described here was approved by the research ethics community at Sheffield Hallam University.
1. Setup
NOTE: Please refer to the attached R script provided as Supplementary File 1.
<ol>
	<li>Load required packages (Rfacebook9, dplyr10, igraph11, and openxlsx12) in R. Packages refer to functions, datasets, or compiled code that allow users to analyze, transform, or extract data.</li>
	<li>Load (external) retention and user data into R as a data frame from a CSV file. 
	NOTE: Retention data refers to the number of days in which a client participates in the offline (traditional) addiction recovery program. It was provided by the administrator of the (offline) recovery program as recorded onto a CSV file with the participant name and number of days they have been involved in the program. The participant name was replaced by the anonymous ID number prior to being imported into R.</li>
</ol>
2. Data extraction from the online community (the social page of an addiction recovery community)
NOTE: This protocol applies to a social media page, but it can be adapted to different types of online communities. In the case of the Rfacebook package, it allows the user to extract data from the social media page into R.
<ol>
	<li>Create a social media (Facebook) access token by following the guide on the referenced website13.</li>
	<li>Create access token in R.</li>
	<li>Using the &#8220;getGroup&#8221; function from Rfacebook, extract data from the social media page of the community of interest (e.g., content of post, number of comments and likes for each post, a unique ID number for each post, etc.). This data is then saved as a data frame. 
	NOTE: A data frame is essentially a table within R used to store data.</li>
	<li>Using the &#8220;getPosts&#8221; function from Rfacebook, along with the Post IDs extracted in step 2.3, extract data about posts likes made on the page.</li>
	<li>Using the &#8220;getPosts&#8221; function from Rfacebook, along with the Post IDs extracted in step 2.3, extract data on the comments made on each post (e.g., user IDs of people commenting the post, when the comment was made, how many likes the post received). This data is then saved as a data frame.</li>
	<li>Using the comment IDs extracted in step 2.5, extract data on the &#8220;comment likes&#8221; made on each post (e.g., user IDs of people liking the comment). This data is then saved as a data frame.</li>
	<li>Combine the posts, post likes, comments, and comment likes data into one data frame.</li>
	<li>Add a monthly breakdown (i.e., month 1 to 8).</li>
</ol>
3. Calculation of social media activity made and received by each client
<ol>
	<li>Calculate the number of posts, comments, post likes and comment likes made by each client.</li>
	<li>Calculate the number of posts, comments, post likes and comment likes received by each client.</li>
	<li>Join the data frame of social media activity made and received by each client to the retention data frame.</li>
	<li>Calculate the difference between posts and comments with likes and no likes.</li>
	<li>Calculate the difference between posts with comments and no comments.</li>
	<li>Join the likes difference data to the retention data.</li>
	<li>Join the comments difference data to the retention data.</li>
	<li>Calculate all the likes made by each client.</li>
	<li>Calculate all the likes received by each client.</li>
	<li>Identify which users did not participate in social media group (i.e., no activity).</li>
</ol>
4. Conducting social network analysis
<ol>
	<li>Create an edge list. An edge list is a list of relationships within the social network, which in this case is based on 1) liking posts and comments and 2) commenting on posts. This is done by looking at two columns within the dataset. The first column contains the anonymous ID of the person making the post, while the second contains the anonymous ID of the person liking or commenting on the post.</li>
	<li>Create a vertex list. A vertex list is a list of all individuals in the group. This is done by converting the two columns in the list of relationships into one column, and removing duplicate anonymous IDs so only the unique anonymous ID is left.</li>
	<li>Using the &#8220;graph.data.frame&#8221; and &#8220;get.adjacency&#8221; functions in the igraph package, create graph and graph matrix objects from the edge and vertex lists.</li>
	<li>Using the &#8220;degree&#8221; and &#8220;betweenness&#8221; functions from the igraph package, obtain the network statistics (degree and betweenness) of the online group.</li>
</ol>
5. Conducting computerized linguistic analysis in LIWC
<ol>
	<li>Export textual social media data (i.e., posts and comments) and post/comment ID column into CSV files.</li>
	<li>Import the CSV files of textual social media data into the Linguistic Inquiry Word Count (LIWC) software.</li>
	<li>Generate LIWC categories and save to new CSV files. Do this by clicking on &#8220;Analyze Text&#8221;, then on &#8220;Excel/CSV file&#8221;, and clicking on the column containing the posts and comments to select the text to be analyzed. After LIWC has completed analyzing the textual data, save the output as a new CSV file.</li>
	<li>Import the LIWC results CSV file into R, and merge with existing data. The data is matched by the post/comment ID column, which exists in both LIWC and existing data frames.</li>
	<li>Calculate total LIWC scores for each user in posts and comments, then join to the retention data.</li>
	<li>Calculate total LIWC scores for each user in all textual data (post and comments combined), then join to the retention data.</li>
	<li>Remove NAs from the retention data data frame.</li>
</ol>
6. Conducting regression analysis (to determine if indicators of engagement with the online community predict retention in the offline recovery program)
<ol>
	<li>Define the independent variables.</li>
	<li>Using the &#8220;lm&#8221; function in base R, conduct linear regression analysis using the retention data as the dependent variable, and LIWC categories, comments, post likes, and comment likes as independent variables.</li>
	<li>Combine regression analysis results into one data frame.</li>
</ol>
7. Creating monthly SNA maps
<ol>
	<li>Prepare data frames for SNA Maps.</li>
	<li>Create an edge list based on monthly cumulative social media activity.</li>
	<li>Create a vertex list based on monthly cumulative social media activity.</li>
	<li>Create graphs and graph matrices based on monthly cumulative social media activity.</li>
	<li>Set the layout of SNA maps based on cumulative social media activity.</li>
	<li>Add colors based on user roles.</li>
	<li>Create SNA maps and save them to a file.</li>
</ol>
8. Calculating monthly cumulative social media activity of the social media group
<ol>
	<li>Calculate monthly cumulative social media activity by staff, clients, and other members of the social media group.</li>
	<li>Calculate monthly cumulative social media activity by all members of the social media group.</li>
	<li>Join the monthly cumulative social media activity data frames together.</li>
</ol>

<ol>
	<li>Cloud, W., Granfield, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Conceptualizing+recovery+capital:+Expansion+of+a+theoretical+construct.">Conceptualizing recovery capital: Expansion of a theoretical construct.</a> Substance Use and Misuse. 43, 1971-1986 (2008).</li><li>Best, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mapping+the+recovery+stories+of+drinkers+and+drug+users+in+Glasgow:+Quality+of+life+and+its+associations+with+measures+of+recovery+capital.">Mapping the recovery stories of drinkers and drug users in Glasgow: Quality of life and its associations with measures of recovery capital.</a> Drug and Alcohol Review. 31 (3), 334-341 (2012).</li><li>Laudet, A. B., White, W. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Recovery+capital+as+prospective+predictor+of+sustained+recovery,+life+satisfaction,+and+stress+among+former+poly-substance+users.">Recovery capital as prospective predictor of sustained recovery, life satisfaction, and stress among former poly-substance users.</a> Substance Use and Misuse. 43 (1), 27-54 (2008).</li><li>Moorhead, S. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+new+dimension+of+health+care:+systematic+review+of+the+uses,+benefits,+and+limitations+of+social+media+for+health+communication.">A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication.</a> Journal of Medical Internet Research. 15, e85 (2013).</li><li>White, M., Dorman, S. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Receiving+social+support+online:+implications+for+health+education.">Receiving social support online: implications for health education.</a> Health Education Research. 16, 693-707 (2001).</li><li>Best, D., Bliuc, A. M., Iqbal, M., Upton, K., Hodgkins, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mapping+social+identity+change+in+online+networks+of+addiction+recovery.">Mapping social identity change in online networks of addiction recovery.</a> Addiction Research and Theory. 26 (3), 163-173 (2018).</li><li>Bliuc, A. M., Best, D., Beckwith, M., Iqbal, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Online+support+communities+in+addiction+recovery.">Online support communities in addiction recovery.</a> Addiction, behavioral change and social identity: The path to resilience and recovery. , 137 (2016).</li><li>Del Boca, F. K., Darkes, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+validity+of+self&#8208;reports+of+alcohol+consumption:+state+of+the+science+and+challenges+for+research.">The validity of self&#8208;reports of alcohol consumption: state of the science and challenges for research.</a> Addiction. 98, 1-12 (2003).</li><li>. Package &#8216;Rfacebook&#8217; Available from: <a target="_blank" href="https://cran.r-project.org/web/packages/Rfacebook/Rfacebook.pdf">https://cran.r-project.org/web/packages/Rfacebook/Rfacebook.pdf</a> (2017)</li><li>. Package &#8216;dplyr&#8217; Available from: <a target="_blank" href="https://cran.r-project.org/web/packages/dplyr/dplyr.pdf">https://cran.r-project.org/web/packages/dplyr/dplyr.pdf</a> (2018)</li><li>. Package &#8216;igraph&#8217; Available from: <a target="_blank" href="https://cran.r-project.org/web/packages/igraph/igraph.pdf">https://cran.r-project.org/web/packages/igraph/igraph.pdf</a> (2018)</li><li>. Package &#8216;openxlsx&#8217; Available from: <a target="_blank" href="https://cran.r-project.org/web/packages/openxlsx/openxlsx.pdf">https://cran.r-project.org/web/packages/openxlsx/openxlsx.pdf</a> (2018)</li><li>. How to get a Facebook access token which never expires Available from: <a target="_blank" href="https://smashballoon.com/custom-facebook-feed/access-token/">https://smashballoon.com/custom-facebook-feed/access-token/</a> (2018)</li><li>Bliuc, A. M., Best, D., Iqbal, M., Upton, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Building+addiction+recovery+capital+through+online+participation+in+a+recovery+community.">Building addiction recovery capital through online participation in a recovery community.</a> Social Science and Medicine. 193, 110-117 (2017).</li><li>Best, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Overcoming+alcohol+and+other+drug+addiction+as+a+process+of+social+identity+transition:+The+Social+Identity+Model+of+Recovery+(SIMOR).">Overcoming alcohol and other drug addiction as a process of social identity transition: The Social Identity Model of Recovery (SIMOR).</a> Addiction Research and Theory. 24, 111-123 (2016).</li><li>Frings, D., Albery, I. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+social+identity+model+of+cessation+maintenance:+Formulation+and+initial+evidence.">The social identity model of cessation maintenance: Formulation and initial evidence.</a> Addictive Behaviors. 44, 35-42 (2015).</li></ol>

The authors have nothing to disclose.

The approach described here is based on a new method of measuring how online group processes can impact retention in an addiction recovery program. Applying this method to an online community of recovery from addiction, it was found that there were four key aspects predicted program retention: being highly involved in the online community, being central in the online social network, positive affect expressed in communication with other members of the online community, and receiving validation from others for contributions to the network14. The findings obtained by using this method support existing theoretical models of recovery. That is, two key models in the recovery literature, the Social Identity Model of Recovery15 and the Social Identity Model of Cessation Maintenance16, both emphasize the importance of active participation in groups which are supportive of recovery. Both models suggest that increased identification and commitment to such groups contribute to lower future contact with using groups and consequent relapse.
As illustrated in our research, the method allowed us to map out trajectories of recovery or change of individual members of the online community14. Visualizations of the online social networks and their evolution over time can provide valuable information about the movement of members of the online community from the periphery to the center of the network and vice-versa (these movements in the network indicate changes in levels of engagement with the online community). In a 2017 study14, interviews with members of the online community who undertook the most significant changes in terms of movement from the periphery to center of the networks were conducted as a way of triangulating our findings based on SNA, computerized linguistic analysis, and regression against retention data. Future studies may focus instead on those members who became disengaged with the online community, on those who never become engaged, or on more direct measures of outcome such as substance use and reoffending. This methodology can further fine-tuned to be used in intervention programs, for example, for assessing the role of moderators in help forums.
There are currently no studies providing evidence on the benefits of the method described here when used by itself (the method described was used in conjunction with retention data and triangulated with qualitative data from interviews with key online community members14), but this approach can provide accurate and bias-free data that can complement self-reporting and other measures in studies of addiction recovery.
This method was applied to examine online social interactions in the context of a social media page established as a complementary form of support to a standard, face-to-face recovery program. However, with minor changes, the method can be used to investigate online social interactions in other types of online communities (online forums, discussion groups, chat rooms, commentary websites, etc.). One of the key advantages of this method is that it can be adapted and applied to contexts beyond communities of addiction recovery to any online community. For example, in our own political psychology research, we use a similar method (developed from the method described here) to capture the quality of online interactions and changes in these interactions between members of far-right online communities. In effect, the method can be applied to any online community in which data in the form of connections between members (as social network linkages) and linguistic content can be extracted.
However, in accessing and working with online data, researchers need to be aware of ethical issues, some which apply to self-reporting and other types of data in general and some which are only encountered in an online environment. In the research described here (which was approved by the research ethics community at Sheffield Hallam University), consent was obtained from the organization managing the recovery program, and strict measures were taken to ensure complete anonymity of participants in the open social media page (e.g., after online and retention data matching, all identifying information was removed from the files and also no potentially self-identifying quotes were used from the publicly accessible online communication).
Close communication with the organization also ensured that the participants in the program were aware of the study and research findings, and one of the researchers met regularly with the group to explain the study and its results. In other cases, however, where online communities are not associated with specific offline programs, it may be harder to determine who should be asked for consent regarding data extraction (applicable especially in unmoderated forums, where people in recovery seek online peer support). While the general principles of ethical research will apply, researchers need to adopt a case-by-case approach to ensure that the extraction and analysis of online data does not pose any significant risks to the participants (e.g., compromising privacy).

The presented method has been designed to capture alcohol and other drugs (AOD) addiction recovery capital in online contexts. In the field of addiction, recovery capital has been defined as &#8220;the sum total of one&#39;s resources that can be brought to bear on the initiation and maintenance of substance misuse cessation&#8221;1. Recovery capital has been primarily measured through self-reports2,3 in face-to-face contexts. This approach provides an alternative method of measuring recovery capital in online contexts by capturing the quality and quantity of online interactions in online communities of recovery.
Given the steady increase in the use of online resources in the form of peer-support in a range of health-related issues4,5, it is necessary to develop new methods to capture the quality of these resources. Online peer support occurs in the form of social interactions in online forums and communities. Supportive social interactions in these online contexts contribute to building recovery capital, which in turn has a positive impact on the recovery process6,7. The method proposed presents a number of advantages over alternative methods. Firstly, it overcomes some of the limitations involving the use of self-report measures in addiction research, particularly around recall and self-presentational biases. While self-report measures are considered to have reasonable levels of reliability and validity, they are susceptible to biases and inaccuracies. To enhance accuracy and minimize bias, it has been recognized that there is a need to increase the use of novel measures and data collection situations designed to avoid or minimize these issues8. By accessing data naturally occurring in contexts where people in various stages of recovery interact spontaneously, and by using analysis methods that can extract meaningful information from these data (able to capture indicators of psychological states), biases due to social desirability (self-presentational) and inaccuracies due to limitations in recall can be reduced or even eliminated. Secondly, this method is highly efficient and cost-effective, as it relies on the extraction of already existing online data (i.e., in open online forums that are publicly accessible).
Described next is the method that was applied to a study of building recovery capital in an online community established to complement a traditional, face-to-face addiction recovery program for addicts in early recovery stages. In this case, online (social media) data were linked to program retention data, but the method can be also used in cases where linkage data is not available or accessible.

<table>
	<tbody>
		<tr>
			<td>LIWC software</td>
			<td>Receptiviti</td>
			<td>https://liwc.wpengine.com/</td>
			<td>computerised linguistic analysis software</td>
		</tr>
		<tr>
			<td>R software</td>
			<td>n/a</td>
			<td>https://www.r-project.org/</td>
			<td>free statistical and data visualisation sofware</td>
		</tr>
	</tbody>
</table>

integrating computerized linguistic and social network analyses to capture addiction recovery capital in an online community

The article describes a new methodology designed with the aim of finding a comprehensive, unobtrusive, and accurate way of capturing social recovery capital development in online communities of recovery from alcohol and drug (AOD) addiction. Recovery capital was conceptualised as both engagement in the online recovery community and identification with the community. To measure recovery capital development, naturally occurring data were extracted from the social media page of a specific recovery program, with the page being set up as a resource for a face-to-face recovery program. To map engagement with the online community, social network analysis (SNA) capturing online social interaction was performed. Social interaction was measured through the linkages between the online contributors/members of the online community as represented by program clients, staff, and supporters from the broader community. To capture markers of social identification with the online community, computerised linguistic analysis of the textual data (content from posts and comments) was conducted. Recovery capital captured in this way was analysed against retention data (a proxy outcome indicator), as days spent in the (face-to-face) recovery program. The online data extracted was linked to participant data in regards to program retention to test prediction of a key recovery outcome. This approach allowed the examination of the role of online support communities and assessment of the association between recovery capital (developed via the online community of recovery) and recovery outcomes.

A detailed description of representative results obtained using this method can be found in our recent work14, which was reviewed and received full approval from the research ethics committee of the institution at which the research was conducted. In the report described here, the study investigated whether online participation in a community of recovery contributes to the recovery process through recovery capital building (as captured by increased levels and quality of online social interactions and positive identity development). In other words, the study examined whether indicators of online recovery capital developed over the eight months of online data assessed and also predicted retention in a recovery program designed for fostering community involvement for addicts in early stages of recovery.
To map how participants interacted online, social network analysis (SNA) using data extracted from the social media page (n = 609) of a recovery community was conducted. A visual representation of the social network and its evolution is presented in Figure 1. The figure illustrates the activity in the online community observed each month for a period of 8 months in the form of connections between all participants in the online community (i.e., commenting on posts, liking posts, and liking comments). The number of connections that an &#8220;agent&#8221; in the network has determines how central they will be in the social network. Computerized linguistic analysis was used to assess the textual data (capturing social identity markers), and linear regression analysis was conducted to determine whether the indicators of recovery capital predicted program retention. These analyses indicated that program retention was indeed predicted by: (a) levels of group validation received in the form of comment likes and all likes received on the social media page, (b) position in the social network (network centrality), and (c) group identity and achievement (as captured by the linguistic content of online communication). The results supported the argument that, overall, positive social interactions between members of an online recovery community are supportive of the recovery process. A summary of those findings is presented below.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58851/58851fig1.jpg" /> 
Figure 1: Monthly representations of the social network of the online community over 8 months suggest changes in the pattern of social interactions between the participants. These representations illustrate how at the start, most of the client members in the online community (clients of the offline recovery program) are mostly disconnected, and it is the program staff and only a small number of clients who drive the online activity. However, this gradually changes, so that after 8 months, the clients are the ones most connected (therefore the most central), with the highest number of connections in the network (figure is adapted from a previous publication)14. <a href="https://www.jove.com/files/ftp_upload/58851/58851fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Descriptive statistics
Participants&#8217; levels of engagement with the online community were measured by computing the contributions of all participants in the online community as number of posts, comments, and likes made by staff, clients, and broader community members. Table 1 presents a breakdown by type of contribution (as made by each category of participant) across 8 months.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Group members&#160;&#160;</td>
			<td>Type of online contribution&#160;</td>
			<td>Month 1</td>
			<td>Month 2</td>
			<td>Month 3</td>
			<td>Month 4</td>
			<td>Month 5</td>
			<td>Month 6</td>
			<td>Month 7</td>
			<td>Month 8</td>
		</tr>
		<tr>
			<td rowspan="3">All</td>
			<td>Posts and comments</td>
			<td>382</td>
			<td>388 (770)</td>
			<td>579 (1349)</td>
			<td>369 (1718)</td>
			<td>530 (2248)</td>
			<td>581 (2829)</td>
			<td>796 (3625)</td>
			<td>674 (4299)</td>
		</tr>
		<tr>
			<td>Post likes given</td>
			<td>1167</td>
			<td>878 (2045)</td>
			<td>1856 (3901)</td>
			<td>1440 (5341)</td>
			<td>1880 (7221)</td>
			<td>1756 (8977)</td>
			<td>2667 (11644)</td>
			<td>1857 (13501)</td>
		</tr>
		<tr>
			<td>Comment likes given</td>
			<td>784</td>
			<td>970 (1604)</td>
			<td>825 (2429)</td>
			<td>171 (2600)</td>
			<td>634 (3234)</td>
			<td>970 (4204)</td>
			<td>825 (5029)</td>
			<td>171 (5200)</td>
		</tr>
		<tr>
			<td rowspan="3">Staff</td>
			<td>Posts and comments</td>
			<td>129</td>
			<td>106 (235)</td>
			<td>170 (405)</td>
			<td>96 (501)</td>
			<td>185 (686)</td>
			<td>176 (862)</td>
			<td>227 (1089)</td>
			<td>316 (1405)</td>
		</tr>
		<tr>
			<td>Post likes given</td>
			<td>188</td>
			<td>147 (335)</td>
			<td>302 (637)</td>
			<td>209 (846)</td>
			<td>385 (1231)</td>
			<td>372 (1603)</td>
			<td>567 (2170)</td>
			<td>511 (2681)</td>
		</tr>
		<tr>
			<td>Comment likes given</td>
			<td>168</td>
			<td>303 (471)</td>
			<td>237 (708)</td>
			<td>69 (777)</td>
			<td>168 (945)</td>
			<td>303 (1248)</td>
			<td>237 (1485)</td>
			<td>69 (1554)</td>
		</tr>
		<tr>
			<td rowspan="3">Clients</td>
			<td>Posts and comments</td>
			<td>145</td>
			<td>155 (300)</td>
			<td>214 (514)</td>
			<td>132 (646)</td>
			<td>208 (854)</td>
			<td>286 (1140)</td>
			<td>419 (1559)</td>
			<td>253 (1812)</td>
		</tr>
		<tr>
			<td>Post likes given</td>
			<td>365</td>
			<td>252 (617)</td>
			<td>415 (1032)</td>
			<td>303 (1335)</td>
			<td>549 (1884)</td>
			<td>529 (2413)</td>
			<td>898 (3311)</td>
			<td>576 (3887)</td>
		</tr>
		<tr>
			<td>Comment likes given</td>
			<td>143</td>
			<td>318 (461)</td>
			<td>235 (696)</td>
			<td>33 (729)</td>
			<td>143 (872)</td>
			<td>318 (1190)</td>
			<td>235 (1425)</td>
			<td>33 (1458)</td>
		</tr>
		<tr>
			<td>Others</td>
			<td>Posts and comments</td>
			<td>108</td>
			<td>127 (235)</td>
			<td>195 (430)</td>
			<td>141 (571)</td>
			<td>137 (708)</td>
			<td>119 (827)</td>
			<td>150 (977)</td>
			<td>105 (1082)</td>
		</tr>
		<tr>
			<td></td>
			<td>Post likes given</td>
			<td>614</td>
			<td>479 (1093)</td>
			<td>1139 (2232)</td>
			<td>928 (3160)</td>
			<td>946 (4106)</td>
			<td>855 (4961)</td>
			<td>1202 (6163)</td>
			<td>770 (6933)</td>
		</tr>
		<tr>
			<td></td>
			<td>Comment likes given</td>
			<td>473</td>
			<td>349 (672)</td>
			<td>353 (1025)</td>
			<td>69 (1094)</td>
			<td>323 (1417)</td>
			<td>349 (1766)</td>
			<td>353 (2119)</td>
			<td>69 (2188)</td>
		</tr>
	</tbody>
</table>
Table 1: Shown is the number of online contributions by type (post and comments made, likes given to posts, and likes given to comments) by members of the online community on the across 8 months. The members of the online community are classified as staff (support staff employed by the offline recovery program), clients (people in recover who are participating in the offline recovery program), and others (supporters and pro-recovery advocates from the broader community).
Determinants of retention in the program 
The following hypotheses were tested: (1) program retention should be associated with indicators of recovery capital development (i.e., reflected in the quantity and quality of online interaction), and (2) program retention should also be associated with indicators of identity change, (i.e., indicators of positive recovery identity development). The quantity of online interaction was indicated by the a) number of posts made, b) number of comments made, c) number of post likes received, d) number of comment likes received, and e) number of all likes received.
To determine the quality of online interaction, network structure and language content were analyzed. More specifically, degree and betweenness coefficients derived from social network analysis (SNA) and linguistic indicators of positive affect derived from computerized linguistic analysis were used. As indicators of positive identity change (as identification with the recovery community) the frequency of use of the pronoun &#8220;we&#8221; and achievement words (e.g., try, goal, win, etc.) were used. Finally, the dependent variable (retention in the program) was indicated by the total number of days spent in the program (ranging from 86 to 464 days here). As shown by the results, levels of online interaction and in-group validation (as reflected by the number of likes received for posts and comments) predicted program retention (Table 2). Program retention was also predicted by identification markers (as captured by the use of the pronoun &#8220;we&#8221; in posts and of achievement words in both posts and comments). Finally, where participants are situated within the social network (i.e., degree of centrality) also represents an important aspect of retention (Table 2).
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Variable</td>
			<td>B</td>
			<td>SE</td>
			<td>&#946;</td>
			<td>R2</td>
		</tr>
		<tr>
			<td>Comment likes received</td>
			<td>0.43</td>
			<td>0.18</td>
			<td>.47*</td>
			<td>0.22</td>
		</tr>
		<tr>
			<td>Likes received (all)</td>
			<td>0.08</td>
			<td>0.03</td>
			<td>.43*</td>
			<td>0.18</td>
		</tr>
		<tr>
			<td>Comment-like difference</td>
			<td>1.09</td>
			<td>0.5</td>
			<td>.43*</td>
			<td>0.19</td>
		</tr>
		<tr>
			<td>Network degree</td>
			<td>0.01</td>
			<td>0</td>
			<td>.43*</td>
			<td>0.18</td>
		</tr>
		<tr>
			<td>LIWC We (Post)</td>
			<td>3.89</td>
			<td>1.76</td>
			<td>.43*</td>
			<td>0.19</td>
		</tr>
		<tr>
			<td>LIWC Achievement (Post)</td>
			<td>0.56</td>
			<td>0.26</td>
			<td>.43*</td>
			<td>0.18</td>
		</tr>
		<tr>
			<td>LIWC Achievement (All)</td>
			<td>0.14</td>
			<td>0.07</td>
			<td>.42*</td>
			<td>0.17</td>
		</tr>
	</tbody>
</table>
Table 2: Retention time as predicted by online engagement, network statistics, and linguistic categories.

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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

The article describes a novel approach for analyzing dynamic online social interactions (in an online context) exemplified by a study of an online community of recovery from alcohol and drug addiction.

The article describes a new methodology designed with the aim of finding a comprehensive, unobtrusive, and accurate way of capturing social recovery ...

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5.1K Views.  Western Sydney University. The article describes a novel approach for analyzing dynamic online social interactions (in an online context) exemplified by a study of an online community of recovery from alcohol and drug addiction.

Video: Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials

Social exclusion is a complex social phenomenon with powerful negative consequences. Given the impact of social exclusion on mental and emotional health, an understanding of how perceptions of social exclusion develop over the course of a social interaction is important for advancing treatments aimed at lessening the harmful costs of being excluded. To date, most scientific examinations of social exclusion have looked at exclusion after a social interaction has been completed. While this has been very helpful in developing an understanding of what happens to a person following exclusion, it has not helped to clarify the moment-to-moment dynamics of the process of social exclusion. Accordingly, the current protocol was developed to obtain an improved understanding of social exclusion by examining the patterns of event-related brain activation that are present during social interactions. This protocol allows greater precision and sensitivity in detailing the social processes that lead people to feel as though they have been excluded from a social interaction. Importantly, the current protocol can be adapted to include research projects that vary the nature of exclusionary social interactions by altering how frequently participants are included, how long the periods of exclusion will last in each interaction, and when exclusion will take place during the social interactions. Further, the current protocol can be used to examine variables and constructs beyond those related to social exclusion. This capability to address a variety of applications across psychology by obtaining both neural and behavioral data during ongoing social interactions suggests the present protocol could be at the core of a developing area of scientific inquiry related to social interactions.

Research on social exclusion has grown tremendously in recent years. As the field expands, it is imperative to develop sophisticated methodologies allowing for the simultaneous measurement of neural and behavioral outcomes during social exclusion. This protocol utilizes event-related brain potentials to record ongoing neural activity during computerized social interactions.

Social exclusion is a complex social phenomenon with powerful negative consequences. Given the impact of social exclusion on mental and emotional health, ...

Methods to Test Visual Attention Online

Online data collection methods have particular appeal to behavioral scientists because they offer the promise of much larger and much more representative data samples than can typically be collected on college campuses. However, before such methods can be widely adopted, a number of technological challenges must be overcome &#8211; in particular in experiments where tight control over stimulus properties is necessary. Here we present methods for collecting performance data on two tests of visual attention. Both tests require control over the visual angle of the stimuli (which in turn requires knowledge of the viewing distance, monitor size, screen resolution, etc.) and the timing of the stimuli (as the tests involve either briefly flashed stimuli or stimuli that move at specific rates). Data collected on these tests from over 1,700 online participants were consistent with data collected in laboratory-based versions of the exact same tests. These results suggest that with proper care, timing/stimulus size dependent tasks can be deployed in web-based settings.

To replicate laboratory settings, online data collection methods for visual tasks require tight control over stimulus presentation. We outline methods for the use of a web application to collect performance data on two tests of visual attention.

Online data collection methods have particular appeal to behavioral scientists because they offer the promise of much larger and much more representative ...

The Modified Hole Board - Measuring Behavior, Cognition and Social Interaction in Mice and Rats

This protocol describes the modified hole board (mHB), which combines features from a traditional hole board and open field and is designed to measure multiple dimensions of unconditioned behavior in small laboratory mammals (e.g., mice, rats, tree shrews and small primates). This paradigm is a valuable alternative for the use of a behavioral test battery, since a broad behavioral spectrum of an animal&#8217;s behavioral profile can be investigated in one single test.
The apparatus consists of a box, representing the &#8216;protected&#8217; area, separated from a group compartment. A board, on which small cylinders are staggered in three lines, is placed in the center of the box, representing the &#8216;unprotected&#8217; area of the set-up. The cognitive abilities of the animals can be measured by baiting some cylinders on the board and measuring the working and reference memory. Other unconditioned behavior, such as activity-related-, anxiety-related- and social behavior, can be observed using this paradigm. Behavioral flexibility and the ability to habituate to a novel environment can additionally be observed by subjecting the animals to multiple trials in the mHB, revealing insight into the animals&#8217; adaptive capacities.
Due to testing order effects in a behavioral test battery, na&#239;ve animals should be used for each individual experiment. By testing multiple behavioral dimensions in a single paradigm and thereby circumventing this issue, the number of experimental animals used is reduced. Furthermore, by avoiding social isolation during testing and without the need to food deprive the animals, the mHB represents a behavioral test system, inducing if any, very low amount of stress.

This protocol describes the modified hole board, which is a behavioral test set-up that comprises the characteristics of an open field and a traditional hole board. This set-up enables the differential analysis of unconditioned behavior of small laboratory mammals as well as the analysis of cognitive abilities.

This protocol describes the modified hole board (mHB), which combines features from a traditional hole board and open field and is designed to measure ...

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device (ALDM) Test Systems

Fagot &#38; Paleressompoulle1 and Fagot &#38; Bonte2 have published an automated learning device (ALDM) for the study of cognitive abilities of monkeys maintained in semi-free ranging conditions. Data accumulated during the last five years have consistently demonstrated the efficiency of this protocol to investigate individual/physical cognition in monkeys, and have further shown that this procedure reduces stress level during animal testing3. This paper demonstrates that networks of ALDM can also be used to investigate different facets of social cognition and in-group expressed behaviors in monkeys, and describes three illustrative protocols developed for that purpose. The first study demonstrates how ethological assessments of social behavior and computerized assessments of cognitive performance could be integrated to investigate the effects of socially exhibited moods on the cognitive performance of individuals. The second study shows that batteries of ALDM running in parallel can provide unique information on the influence of the presence of others on task performance. Finally, the last study shows that networks of ALDM test units can also be used to study issues related to social transmission and cultural evolution. Combined together, these three studies demonstrate clearly that ALDM testing is a highly promising experimental tool for bridging the gap in the animal literature between research on individual cognition and research on social cognition.

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems

Fagot &#38; Paleressompoulle1 have published an automated learning device (ALDM) aimed at testing individual cognitive abilities in semi-free ranging monkeys. The main goal of our protocol is to use a network of ALDM test units to study social cognition in non-human primates.

Fagot &#38; Paleressompoulle1 and Fagot &#38; Bonte2 have published an automated learning device (ALDM) for the study of cognitive abilities of monkeys ...

The Social Dimension of Stress: Experimental Manipulations of Social Support and Social Identity in the Trier Social Stress Test

In many situations humans are influenced by the behavior of other people and their relationships with them. For example, in stressful situations supportive behavior of other people as well as positive social relationships can act as powerful resources to cope with stress. In order to study the interplay between these variables, this protocol describes two effective experimental manipulations of social relationships and supportive behavior in the laboratory. In the present article, these two manipulations are implemented in the Trier Social Stress Test (TSST)&#8212;a standard stress induction paradigm in which participants are subjected to a simulated job interview. More precisely, we propose (a) a manipulation of the relationship between different protagonists in the TSST by making a shared social identity salient and (b) a manipulation of the behavior of the TSST-selection committee, which acts either supportively or unsupportively. These two experimental manipulations are designed in a modular fashion and can be applied independently of each other but can also be combined. Moreover, these two manipulations can also be integrated into other stress protocols and into other standardized social interactions such as trust games, negotiation tasks, or other group tasks.

Previous research on the social dimension of stress has focused on two important variables: social identity and social support. This protocol introduces an effective experimental manipulation of these two social variables and describes their implementation in a standard stress induction paradigm (Trier Social Stress Test).

In many situations humans are influenced by the behavior of other people and their relationships with them. For example, in stressful situations ...

Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm

The ability to recall the past allows us to report on details of previous experiences, from the everyday to the significant. Because recall memory is commonly assessed using verbal report paradigms in adults, studying the development of this ability in preverbal infants and children proved challenging. Over the past 30 years, researchers have developed a non-verbal means of assessing recall memory known as the elicited or deferred imitation paradigm. In one variant of the procedure, participants are presented with novel three-dimensional stimuli for a brief baseline period before a researcher demonstrates a series of actions that culminate in an end- or goal-state. The participant is allowed to imitate the demonstrated actions immediately, after a delay, or both. Recall performance is then compared to baseline or to performance on novel control sequences presented at the same session; memory can be assessed for the individual target actions and the order in which they were completed. This procedure is an accepted analogue to the verbal report techniques used with adults, and it has served to establish a solid foundation of the nature of recall memory in infancy and early childhood. In addition, the elicited or deferred imitation procedure has been modified and adapted to answer questions relevant to other aspects of cognitive functioning. The broad utility and application of imitation paradigms is discussed, along with limitations of the approach and directions for future research.

The elicited imitation procedure was established to examine the development of recall memory in infancy and early childhood. This procedure has been widely used to establish a solid foundation of the nature of recall memory in infancy and early childhood.

The ability to recall the past allows us to report on details of previous experiences, from the everyday to the significant. Because recall memory is ...

A Visual Guide for Studying Behavioral Defenses to Pathogen Attacks in Leaf-Cutting Ants

The complex lifestyle, evolutionary history of advanced cooperation, and disease defenses of leaf-cutting ants are well studied. Although numerous studies have described the behaviors connected with disease defense, and the associated use of chemicals and antimicrobials, no common visual reference has been made. The main aim of this study was to record short clips of behaviors involved in disease defense, both prophylactically and directly targeted towards an antagonist of the colony following infection. To do so we used an infection experiment, with sub-colonies of the leaf-cutting ant species Acromyrmex echinatior, and the most significant known pathogenic threat to the ants&#39; fungal crop (Leucoagaricus gongylophorus), a specialized pathogenic fungus in the genus Escovopsis. We filmed and compared infected and uninfected colonies, at both early and more advanced stages of infection. We quantified key defensive behaviors across treatments and show that the behavioral response to pathogen attack likely varies between different castes of worker ants, and between early and late detection of a threat. Based on these recordings we have made a library of behavioral clips, accompanied by definitions of the main individual defensive behaviors. We anticipate that such a guide can provide a common frame of reference for other researchers working in this field, to recognize and study these behaviors, and also provide greater scope for comparing different studies to ultimately help better understand the role these behaviors play in disease defense.

We present a visual guide to disease defense behaviors in leaf-cutting ants, with individual clips and accompanying definitions, illustrated in an experimental infection scenario. Our main aim is to help other researchers recognize key defensive behaviors and to provide a common understanding for future research in this field.

The complex lifestyle, evolutionary history of advanced cooperation, and disease defenses of leaf-cutting ants are well studied. Although numerous studies ...

Using Facial Electromyography to Assess Facial Muscle Reactions to Experienced and Observed Affective Touch in Humans

"Affective" touch is believed to be processed in a manner distinct from discriminatory touch and to involve activation of C-tactile (CT) afferent fibers. Touch that optimally activates CT fibers is consistently rated as hedonically pleasant. Patient groups with impaired social-emotional functioning also show disordered affective touch ratings. However, relying on self-reported ratings of touch has many limitations, including recall bias and communication barriers. Here, we describe a methodological approach to study affective responses to touch via facial electromyography (EMG) that circumvents the reliance on self-report ratings. Facial EMG is an objective, quantitative, and non-invasive method to measure facial muscle activity indicative of affective responses. Responses can be assessed across healthy and patient populations without the need for verbal communication. Here, we provide two separate datasets demonstrating that CT-optimal and non-optimal touch elicit distinct facial muscle reactions. Moreover, facial EMG responses are consistent across stimulus modalities, e.g. tactile (experienced touch) and visual (observed touch). Finally, the temporal resolution of facial EMG can detect responses on timescales that supersede that of verbal reporting. Together, our data suggest that facial EMG is a suitable methodology for use in affective tactile research that can be used to supplement, or in some cases, supplant, existing measures.

We describe a protocol to assess facial muscle activity in response to experienced and observed tactile stimulation using facial electromyography.

"Affective" touch is believed to be processed in a manner distinct from discriminatory touch and to involve activation of C-tactile (CT) afferent fibers. ...

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Mild cognitive impairment (MCI) is the first sign of dementia among elderly populations and its early detection is crucial in our aging societies. Common MCI tests are time-consuming such that indiscriminate massive screening would not be cost-effective. Here, we describe a protocol that uses machine learning techniques to rapidly select candidates for further screening via a question-based MCI test. This minimizes the number of resources required for screening because only patients who are potentially MCI positive are tested further.
This methodology was applied in an initial MCI research study that formed the starting point for the design of a selective screening decision tree. The initial study collected many demographic and lifestyle variables as well as details about patient medications. The Short Portable Mental Status Questionnaire (SPMSQ) and the Mini-Mental State Examination (MMSE) were used to detect possible cases of MCI. Finally, we used this method to design an efficient process for classifying individuals at risk of MCI. This work also provides insights into lifestyle-related factors associated with MCI that could be leveraged in the prevention and early detection of MCI among elderly populations.

This methodology produces decision trees that target population groups more prone to suffering from mild cognitive impairment and are useful for cost-effective selective screening of the disease.

Mild cognitive impairment (MCI) is the first sign of dementia among elderly populations and its early detection is crucial in our aging societies. Common ...

Transcranial Direct Current Stimulation for Online Gamers

Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique that applies a weak electric current to the scalp to modulate neuronal membrane potentials. Compared to other brain stimulation methods, tDCS is relatively safe, simple, and inexpensive to administer.
Since excessive online gaming can negatively affect mental health and daily functioning, developing treatment options for gamers is necessary. Although tDCS over the dorsolateral prefrontal cortex (DLPFC) has demonstrated promising results for various addictions, it has not been tested in gamers. This paper describes a protocol and a feasibility study for applying repeated tDCS over the DLPFC and neuroimaging to examine the underlying neural correlates in gamers.
At baseline, individuals who play online games report average weekly hours spent on games, complete questionnaires on addiction symptoms and self-control, and undergo brain 18F-&#64258;uoro-2-deoxyglucose positron emission tomography (FDG-PET). The tDCS protocol consists of 12 sessions over the DLPFC for 4 weeks (anode F3/cathode F4, 2 mA for 30 min per session). Then, a follow-up is conducted using the same protocol as the baseline. Individuals who do not play online games receive only baseline FDG-PET scans without tDCS. Changes of clinical characteristics and asymmetry of regional cerebral metabolic rate of glucose (rCMRglu) in the DLPFC are examined in gamers. In addition, asymmetry of rCMRglu is compared between gamers and non-gamers at baseline.
In our experiment, 15 gamers received tDCS sessions and completed baseline and follow-up scans. Ten non-gamers underwent FDG-PET scans at the baseline. The tDCS reduced addiction symptoms, time spent on games, and increased self-control. Moreover, abnormal asymmetry of rCMRglu in the DLPFC at baseline was alleviated after tDCS.
The current protocol may be useful for assessing treatment efficacy of tDCS and its underlying brain changes in gamers. Further randomized sham-controlled studies are warranted. Moreover, the protocol can be applied to other neurological and psychiatric disorders.

We present a protocol and a feasibility study for applying transcranial direct current stimulation (tDCS) and neuroimaging assessment in online gamers.

Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique that applies a weak electric current to the scalp to modulate ...