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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SCIENTISTS IN ACTION

5.1K Views.  Western Sydney University. Ons protocol biedt een uitgebreide, onopvallende en nauwkeurige manier om te beschrijven hoe mensen kunnen omgaan met online gemeenschappen terwijl ze herstellen van drugs- en alcoholverslaving. Deze techniek gaat verder dan de zelfgerapporteerde benadering die is gebruikt in eerdere studies, waardoor we meer impliciete markers van herstel kunnen meten. Voordat u met de extractie begint, laadt u de vereiste functies, gegevenssets of gecompileerde code waarmee gebruikers gegevens in R kunnen a...

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

Comprehensive Analysis of Transcription Dynamics from Brain Samples Following Behavioral Experience

The encoding of experiences in the brain and the consolidation of long-term memories depend on gene transcription. Identifying the function of specific genes in encoding experience is one of the main objectives of molecular neuroscience. Furthermore, the functional association of defined genes with specific behaviors has implications for understanding the basis of neuropsychiatric disorders. Induction of robust transcription programs has been observed in the brains of mice following various behavioral manipulations. While some genetic elements are utilized recurrently following different behavioral manipulations and in different brain nuclei, transcriptional programs are overall unique to the inducing stimuli and the structure in which they are studied1,2.
In this publication, a protocol is described for robust and comprehensive transcriptional profiling from brain nuclei of mice in response to behavioral manipulation. The protocol is demonstrated in the context of analysis of gene expression dynamics in the nucleus accumbens following acute cocaine experience. Subsequent to a defined in vivo experience, the target neural tissue is dissected; followed by RNA purification, reverse transcription and utilization of microfluidic arrays for comprehensive qPCR analysis of multiple target genes. This protocol is geared towards comprehensive analysis (addressing 50-500 genes) of limiting quantities of starting material, such as small brain samples or even single cells.
The protocol is most advantageous for parallel analysis of multiple samples (e.g. single cells, dynamic analysis following pharmaceutical, viral or behavioral perturbations). However, the protocol could also serve for the characterization and quality assurance of samples prior to whole-genome studies by microarrays or RNAseq, as well as validation of data obtained from whole-genome studies.

This manuscript describes a protocol that applies comprehensive profiling for analysis of transcriptional programs induced in specific brain nuclei of rodents following behavioral paradigms. Herein, this approach is illustrated in the context of profiling genes induced in the nucleus accumbens (NAc) of mice following acute cocaine exposure, utilizing microfluidic qPCR arrays.

The encoding of experiences in the brain and the consolidation of long-term memories depend on gene transcription. Identifying the function of specific ...

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, ...

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 ...

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the daily life of older adults over extended periods of time. The technology is aggregated into a system that incorporates the principles of being minimally obtrusive, while generating secure, privacy protected, continuous objective data in real-world (home-based) settings for months to years. The system includes passive infrared presence sensors placed throughout the home, door contact sensors installed on exterior doors, connected physiological monitoring devices (such as scales), medication boxes, and wearable actigraphs. Driving sensors are also installed in participants' cars and computer (PC, tablet or smartphone) use is tracked. Data is annotated via frequent online self-report options that provide vital information with regard to the data that is difficult to infer via sensors such as internal states (e.g., pain, mood, loneliness), as well as data referent to activity pattern interpretation (e.g., visitors, rearranged furniture). Algorithms have been developed using the data obtained to identify functional domains key to health or disease activity monitoring, including mobility (e.g., room transitions, steps, gait speed), physiologic function (e.g., weight, body mass index, pulse), sleep behaviors (e.g., sleep time, trips to the bathroom at night), medication adherence (e.g., missed doses), social engagement (e.g., time spent out of home, time couples spend together), and cognitive function (e.g., time on computer, mouse movements, characteristics of online form completion, driving ability). Change detection of these functions provides a sensitive marker for the application in health surveillance of acute illnesses (e.g., viral epidemic) to the early detection of prodromal dementia syndromes. The system is particularly suitable for monitoring the efficacy of clinical interventions in natural history studies of geriatric syndromes and in clinical trials.

Unobtrusive sensors and pervasive computing technology incorporated into the daily home life of older adults enables meaningful health and activity changes to be recorded continuously for months to years, providing ecologically valid, high frequency, multi-domain data for research or clinical use.

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the ...

Combining Laser Capture Microdissection and Microfluidic qPCR to Analyze Transcriptional Profiles of Single Cells: A Systems Biology Approach to Opioid Dependence

Profound transcriptional heterogeneity in anatomically adjacent single cells suggests that robust tissue functionality may be achieved by cellular phenotype diversity. Single-cell experiments investigating the network dynamics of biological systems demonstrate cellular and tissue responses to various conditions at biologically meaningful resolution. Herein, we explain our methods for gathering single cells from anatomically specific locations and accurately measuring a subset of their gene expression profiles. We combine laser capture microdissection (LCM) with microfluidic reverse transcription quantitative polymerase chain reactions (RT-qPCR). We also use this microfluidic RT-qPCR platform to measure the microbial abundance of gut contents.

This protocol explains how to collect single neurons, microglia, and astrocytes from the central nucleus of the amygdala with high accuracy and anatomic specificity using laser capture microdissection. Additionally, we explain our use of microfluidic RT-qPCR to measure a subset of the transcriptome of these cells.

Profound transcriptional heterogeneity in anatomically adjacent single cells suggests that robust tissue functionality may be achieved by cellular ...

Genetics

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 ...

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Eating disorders (anorexia nervosa, bulimia nervosa, binge-eating disorder, and other specified eating or feeding disorders) have a combined prevalence of 13% and are associated with severe physical and psychosocial problems. Early diagnosis, which is important for effective treatment and prevention of undesirable long-term health consequences, imposes problems among non-specialist clinicians unfamiliar with these patients, such as those working in primary care. Early, accurate diagnosis, particularly in primary care, allows expert interventions early enough in the disorder to facilitate positive treatment outcomes. Computer-assisted diagnostic procedures offer a possible solution to this problem by providing expertise via an algorithm that has been developed from a large number of cases that have been diagnosed in person by expert diagnosticians and expert caregivers. A web-based system for determining an accurate diagnosis for patients suspected to suffer from an eating disorder was developed based on these data. The process is automated using an algorithm that estimates the respondent's probability of having an eating disorder and the type of eating disorder the individual has. The system provides a report that works as an aid for clinicians during the diagnostic process and serves as an educational tool for new clinicians.

Diagnosing eating disorders in healthcare is challenging. Therefore, the present protocol develops an algorithm based on 949 patient responses to a questionnaire, with the diagnosis displayed on an easy-to-use web-based interface. This system facilitates the accurate diagnosis of eating disorders while excluding those believed to have an eating disorder.

Eating disorders (anorexia nervosa, bulimia nervosa, binge-eating disorder, and other specified eating or feeding disorders) have a combined prevalence of ...

Investigating Drivers of Antireward in Addiction Behavior with Anatomically Specific Single-Cell Gene Expression Methods

Increasing rates of addiction behavior have motivated mental health researchers and clinicians alike to understand antireward and recovery. This shift away from reward and commencement necessitates novel perspectives, paradigms, and hypotheses along with an expansion of the methods applied to investigate addiction. Here, we provide an example: A systems biology approach to investigate antireward that combines laser capture microdissection (LCM) and high-throughput microfluidic reverse transcription quantitative polymerase chain reactions (RT-qPCR). Gene expression network dynamics were measured and a key driver of neurovisceral dysregulation in alcohol and opioid withdrawal, neuroinflammation, was identified. This combination of technologies provides anatomic and phenotypic specificity at single-cell resolution with high-throughput sensitivity and specific gene expression measures yielding both hypothesis-generating datasets and mechanistic possibilities that generate opportunities for novel insights and treatments.

The combination of laser capture microdissection and microfluidic RT-qPCR provides anatomic and biotechnical specificity in measuring the transcriptome in single neurons and glia. Applying creative methods with a system's biology approach to psychiatric disease may lead to breakthroughs in understanding and treatment such as the neuroinflammation antireward hypothesis in addiction.

Increasing rates of addiction behavior have motivated mental health researchers and clinicians alike to understand antireward and recovery. This shift ...

Neuroscience

Modeling Alcohol Use Disorder Using Chronic Intermittent Ethanol Vapor Exposure

Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder

Alcohol use disorder (AUD) is a chronic alcohol-related disorder that typically presents as uncontrolled drinking and preoccupation with alcohol. A key component of AUD research is using translationally relevant preclinical models. Over the past several decades, a variety of animal models have been used to study AUD. One prominent model of AUD is the chronic intermittent ethanol vapor exposure (CIE) model, which is a well-established approach for inducing alcohol dependence in rodents through repeated cycles of ethanol exposure via inhalation. To model AUD in mice, the CIE exposure is paired with a voluntary two-bottle choice (2BC) of alcohol drinking and water to measure the escalation of alcohol drinking. The 2BC/CIE procedure involves alternating weeks of 2BC drinking and CIE, which repeat until the escalation of alcohol drinking is achieved. In the present study, we outline the procedures for performing 2BC/CIE, including the daily use of the CIE vapor chamber, and provide an example of escalated alcohol drinking in C57BL/6J mice using this approach.

Presented here is a protocol for the 2BC/CIE model of alcohol dependence in mice to study alcohol use disorder.

Alcohol use disorder (AUD) is a chronic alcohol-related disorder that typically presents as uncontrolled drinking and preoccupation with alcohol. A key ...

Sex-Specific Glial Biomarkers in Alzheimer&#39;s Disease: A Transcriptome Analysis

Biomarker Identification for Gender Specificity of Alzheimer's Disease Based on the Glial Transcriptome Profiles

Many sex-specific biomarkers have been recently revealed in Alzheimer&#39;s disease (AD); however, cerebral glial cells were rarely reported. This study analyzed 220,095 single-nuclei transcriptomes from the frontal cortex of thirty-three AD individuals in the GEO database. Sex-specific Differentially Expressed Genes (DEGs) were identified in glial cells, including 243 in astrocytes, 1,154 in microglia, and 572 in oligodendrocytes. Gene Ontology (GO) functional annotation analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed functional concentration in synaptic, neural, and hormone-related pathways. Protein-protein interaction network (PPI) identified MT3, CALM2, DLG2, KCND2, PAKACB, CAMK2D, and NLGN4Y in astrocytes, TREM2, FOS, APOE, APP, and NLGN4Y in microglia, and GRIN2A, ITPR2, GNAS, and NLGN4Y in oligodendrocytes as key genes. NLGN4Y was the only gene shared by the three glia and was identified as the biomarker for the gender specificity of AD. Gene-transcription factor (TF)-miRNA coregulatory network identified key regulators for NLGN4Y and its target TCMs. Ecklonia kurome Okam (Kunbu) and Herba Ephedrae (Mahuang) were identified, and the effects of the active ingredients on AD were displayed. Finally, enrichment analysis of Kunbu and Mahuang suggested that they might act as therapeutic candidates for gender specificity of AD.

Author Spotlight: Exploring Sex-Specific Glial Signatures and Therapeutic Leads for Alzheimer&#39;s Disease

This study analyzed single-nuclei transcriptomes of thirty-three individuals with Alzheimer's disease (AD), revealing sex-specific DEGs in glial cells. Functional enrichment analysis highlighted synaptic, neural, and hormone-related pathways. Key genes, namely NLGN4Y and its regulators, were identified, and potential therapeutic candidates for gender-specific AD were proposed.

Many sex-specific biomarkers have been recently revealed in Alzheimer&#39;s disease (AD); however, cerebral glial cells were rarely reported. This study ...

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

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